Chapter 13: Misinformation & Deepfakes

In January 2026, Canadian and international reporting documented a wave of non-consensual sexualized deepfake images of identifiable women generated with…

In January 2026, Canadian and international reporting documented a wave of non-consensual sexualized deepfake images of identifiable women generated with X's Grok AI, and, as Global News reported in covering the same scandal, AI-generated child sexual abuse material on the platform. Canada's Privacy Commissioner opened, and then expanded, an investigation into X over the images. Minister Solomon's response was to point to Bill C-16, the Protecting Victims Act introduced by Justice Minister Sean Fraser on December 9, 2025 and amended in May 2026 (adding a 48-hour platform takedown duty), now advancing through Parliament, which extends Canadian criminal law to cover sexualized deepfakes for the first time. The case illustrates both what Canadian deepfake regulation now reaches and what it doesn't. In April and May 2025, Canada held a federal election. Researchers from multiple institutions analyzed political content circulating during the campaign across X, Bluesky, and Reddit: 187,778 posts in total. Approximately 5.9% of election-related images were deepfakes. Right-leaning accounts posted deepfakes at roughly twice the rate of left-leaning accounts. Most of the deepfakes were benign or non-political. The harmful ones, defamatory or conspiratorial, drew limited reach, accounting for only 0.12% of total views on X. Canada has no legislation specifically addressing political deepfakes. Bill C-16 covers only sexual deepfakes. > GOBLIN FACTS — the election evidence is narrower than the panic. The 2025 Canadian election study found a 5.9% deepfake rate in the observed image dataset and limited reach for the harmful subset. Real, measurable, and still not the whole election. This chapter takes the deepfake landscape as it actually exists in Canada in 2026, empirically grounded rather than speculatively framed, and works through what current law covers, what it doesn't, what the international comparators look like, and what the NIL Rights / likeness rights convergence introduced in Chapter 11 offers as a policy path. The chapter is structurally similar to Chapter 11 in its presentation of substantive disagreement, and structurally similar to Chapter 10 in its mapping of regulatory gaps. The empirical anchor, the 2025 Canadian election deepfake study, is the chapter's central evidence base. You will leave with: the Bill C-16 framework and its specific coverage; the 2025 Canadian election empirical findings; the political-deepfake regulatory gap named plainly; the international comparison (US TAKE IT DOWN, UK Online Safety Act, Australia, EU AI Act) framing Canada's position; the Quebec leadership pattern returning here for the third or fourth time; the convergent NIL Rights / likeness rights mechanism as the specific policy path; and the working test for evaluating any deepfake-related claim. ---

Bill C-16 — what it covers

The Protecting Victims Act (Bill C-16) was introduced in the House of Commons on December 9, 2025 by Justice Minister Sean Fraser; after amendments in May 2026 (which added a 48-hour platform takedown duty, "nearly nude" coverage, and higher penalties for assault-related deepfakes) it received Royal Assent on June 18, 2026, with most provisions coming into force July 18, 2026. The bill is 158 pages long. The deepfake provisions are not the entire bill. It also addresses femicide classification (creating a separate first-degree murder category for femicide), coercive control in intimate partner relationships, and revisions to several other Criminal Code provisions. The deepfake-specific provisions are concentrated in amendments to Section 162.1 of the Criminal Code (non-consensual distribution of intimate images, originally added to the Criminal Code in 2014).

The specific changes Bill C-16 makes:

Explicit coverage of deepfakes. Section 162.1 was originally drafted to cover non-consensual distribution of actual intimate images — photographs or videos that had been taken and were being distributed without consent of the person depicted. The 2025 amendments extend the section to cover synthetic intimate images that depict an identifiable person, even when no original intimate image of that person ever existed. This is the core operational extension: criminalizing AI-generated sexualized images of identifiable individuals.

"Nearly nude" inclusion. The amendments expand the offence definition to include images depicting an identifiable person in a "nearly nude" state — broader than the prior law's threshold of nudity or explicit sexual activity. The broader threshold addresses the documented practice of generating partial-undress deepfakes that didn't meet the prior nudity standard but produced similar harm.

Extended maximum penalty. The maximum penalty for the offence increases from 5 years to 10 years imprisonment. This is a substantial legislative signal of escalated seriousness.

New offence for threatening to disclose. A separate new offence criminalizes the threat to disclose intimate images (whether actual or synthetic) without consent: the "sextortion" provision. This addresses cases where the threat itself, rather than actual distribution, is the harm.

Youth involvement. A new offence prohibits involving youth (under 18) in the commission of any of these offences, with separate penalties applying when the offender is an adult and the youth is involved in production or distribution.

How it gets enforced. The core of the bill works through criminal law: the Crown can prosecute offenders, the law applies across Canada, and the enforcement mechanism is the standard policing-and-prosecution pipeline. But as of the May 2026 committee amendments, the bill is no longer criminal-law-only. It now adds a platform takedown duty (services must remove flagged non-consensual intimate images, including AI-generated and "nearly nude" depictions, within 48 hours), which moves Canada onto the same specific footing as the US TAKE IT DOWN Act, the EU, and Australia on rapid removal. What the bill still does not create is a broader civil resolution regime (the statutory damages a victim can sue for directly, the way Quebec's regime allows) or a standalone regulator with deepfake-specific authority. So the picture is mixed rather than bleak: the criminal route still depends on victims reporting to police, police investigating, Crown prosecutors charging, and courts convicting (slow, and victim-initiated), but a rapid platform-removal mechanism now sits beside it. That is a real narrowing of the gap earlier drafts flagged here, with one caveat: a 48-hour duty is only as good as its enforcement, and taking an image down is not the same remedy as punishing the person who made it.

What Bill C-16 does not cover. The bill is explicitly limited to sexualized deepfakes. It does not cover:

  • Political deepfakes (fake images of politicians, electoral interference content)
  • Defamatory deepfakes that are not sexualized (fake quotes, fake speeches, fake actions)
  • Commercial deepfakes (unauthorized commercial use of likeness, fake endorsements)
  • Identity-fraud deepfakes (deepfake-assisted scams targeting individuals)
  • Generic non-consensual deepfakes that don't fall within the specific Section 162.1 scope

These gaps are deliberate. The bill is designed as a narrow extension of an existing criminal provision, not as comprehensive deepfake regulation. The narrow framing is itself a policy choice. Comprehensive deepfake legislation would require either broader criminal provisions or civil regulation that Canada has not enacted. Bill C-16's narrowness reflects the Carney government's choice to extend existing law in a way that addresses the most-documented harm category (sexualized deepfakes, where women and children are disproportionately victimized) without taking on the broader political and commercial regulation that more comprehensive legislation would require.

The dead Online Harms Act backstory. Worth naming because it shapes how readers should understand Bill C-16. The Trudeau government's Online Harms Act attempted comprehensive online-harms regulation: a regulator, mandatory platform takedown, broader categories of regulated content. The Act died on the House floor in 2025 alongside AIDA. Bill C-16 is the Carney government's narrower, criminal-law-centred successor (with a platform-takedown duty added by amendment in May 2026, but none of the broader online-harms regulator or content categories the dead Act had aimed at). The political message: Canada tried a comprehensive online-harms framework, failed, and retreated to a narrower deepfake-specific criminal-law approach. That retreat shapes everything else in the current Canadian regulatory landscape on deepfakes specifically and on platform content regulation generally.

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The 2025 Canadian election empirical study

The most consequential single piece of evidence for this chapter is a study titled "Deepfakes in the 2025 Canadian Election: Prevalence, Partisanship, and Platform Dynamics," published in the Proceedings of the ACM Web Conference 2026 (also available at arxiv:2512.13915). The study analyzed political content circulating during the April–May 2025 Canadian federal election across three major social platforms (X, Bluesky, and Reddit) using the OpenFake dataset (Livernoche et al. 2025, containing over 3 million real images paired with approximately 1 million synthetic counterparts) as the detection baseline.

The empirical findings, in their own framing:

Prevalence. Approximately 5.9% of election-related images circulating on the analyzed platforms during the election period were synthetic (deepfakes). This is the headline number, the empirically observed deepfake rate in the Canadian election information environment. It is higher than zero (the political-deepfake threat is real) and lower than the doomsday framings often used in media coverage (the information environment was not flooded with deepfakes).

Partisan asymmetry. Right-leaning accounts posted at an 8.66% deepfake rate; left-leaning accounts at 4.42%, roughly double. This is a documented partisan asymmetry, not a speculative one.

Content character. Among the deepfakes detected, the researchers found that most were benign or non-political: humorous content, art, satire, clearly-marked manipulation. The deepfakes with defamatory or conspiratorial content (the kind that would constitute the most concerning electoral interference) were a minority of total deepfakes.

Reach. The harmful deepfakes, the defamatory and conspiratorial subset, drew approximately 0.12% of total views on X during the analyzed period. Most users were not encountering them in significant volume. Realistic, defamatory deepfakes that did achieve engagement drew higher per-post engagement than typical content, but the overall reach was limited by detection, platform-side filtering, and user behaviour.

The qualitative finding worth foregrounding. The Canadian Digital Media Research Network (CDMRN), partnered with the Atlantic Council's Digital Forensic Research Lab (DFRLab), identified seven specific deepfakes of Mark Carney during the campaign — videos mimicking CBC or CTV news interview formats, with synthetic-Carney directing viewers to scam websites. These were not political-interference deepfakes in the conventional sense (they didn't aim to influence vote choice); they were financial-scam deepfakes using political celebrity as the credibility vector. The category matters because it sits in the gap between Bill C-16 (which covers sexual deepfakes), conventional electoral integrity law (which covers explicit election interference), and consumer protection law (which covers financial fraud). The Carney scam deepfakes fall outside the focused scope of all three.

What DFRLab found in the structure. DFRLab's April 29, 2025 analysis "How Social Media Shaped the 2025 Canadian Election" documents that Canadians were unable to view basic news reporting on Meta platforms during the election, a consequence of Meta's 2023 decision to block Canadian news in response to the Online News Act, while election-related deepfakes and falsehoods remained freely accessible on the same platforms. This is the structural transparency-failure pattern: legitimate journalism blocked, manipulated content circulating. The platform-economy implications run through Chapter 18 (Transparency); the immediate point for Chapter 13 is that the deepfake exposure rate occurs in an information environment where the comparator content (verified journalism) had been structurally restricted.

The government-side perspective. The Security and Intelligence Threats to Elections (SITE) Task Force's Nathalie Drouin (National Security and Intelligence Advisor) and David Morrison (Deputy Minister of Foreign Affairs) testified publicly after the election that government officials "had expected attempts to use AI to interfere in the last election, but did not detect [significant interference]." The testimony also flagged warnings about the next election facing AI-assisted interference at substantially higher scale. The intelligence-community framing: the 2025 election was not the deepfake catastrophe some predicted, but the absence of catastrophe was partly luck and partly the maturity gap of current AI tools, and the next election cycle will likely face more sophisticated deployment.

A case beyond the ballot. The election was not the only Canadian information fight AI tools were brought to. In 2026, Cipher AI — an Edmonton-and-Regina startup spun out of the University of Regina's Centre for Artificial Intelligence, Data, and Conflict — used AI to track foreign disinformation amplifying the Alberta-separatism debate, attributing campaigns to Russian networks and to US influencers with large followings. The finding worth holding onto is the one Cipher itself foregrounded: the foreign activity amplified the separatist moment but did not manufacture it. That distinction is this chapter's whole discipline applied to a live domestic case. "Foreign bots are behind it" is the doomsday framing; "foreign actors are exploiting a real and homegrown grievance" is what the evidence supported. The same caution cuts the other way, too: the AI-tracking capability that surfaces foreign amplification can also be misused to wave away authentic domestic dissent as someone else's psyop, which is why the amplify-versus-manufacture line has to be stated as plainly as the threat.

The synthesis the evidence supports. The 2025 Canadian election deepfake landscape was real but limited, and asymmetric in who produced it. Most of the content was benign, and the harmful subset reached few people. Platform decisions (Meta's news block) shaped what users encountered, and intelligence officials had anticipated the whole thing and flagged the escalation risk for next time. The information environment was neither catastrophic nor unaffected. "Deepfakes broke the election" overstates what the evidence shows, and so does "deepfakes had no effect." The empirical picture sits in the middle, with specific findings that policy can engage rather than abstract claims that resist evaluation.

ALIGNMENT — how common before how scary. One convincing deepfake makes a great headline and a terrible measurement. Before accepting that something is "everywhere," ask for the base rate: out of how many, over what stretch of time, reaching whom? Fear scales with the vivid example. Reality scales with the denominator.
🧌 GOBLIN CHECK — a personal one. The About the Author section promised this chapter would return to Strange Harvest, so here it is, receipts and all. Our film's festival cut included roughly thirty seconds of licensed, disclosed AI-generated stills used as placeholders during the festival run. The backlash was immediate, so we pulled the material for the theatrical release. Here's the part that belongs in this chapter: the accusations didn't stop — they migrated. Commenters stayed certain, now pointing at sequences that were entirely practical, or at compositing that was merely budget-priced. The lesson the study above documents at scale, I got to live at retail: detection panic doesn't track what's actually synthetic. A label, once applied, doesn't come off. And the audience most confident it could "always tell" performed, on my film, no better than a coin flip. So when you read the 5.9% prevalence number, hold the other side of the coin too: a public that can't reliably spot the 5.9% also can't reliably clear the other 94.1%. The harm isn't only fake content believed. It's real work disbelieved. — A.Y.

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The political deepfake regulatory gap

Putting Sections 1 and 2 together produces a finding worth foregrounding plainly: Canada has criminal legislation covering sexualized deepfakes and essentially no specific legislation covering political deepfakes. Bill C-16 addresses one category; the other category proceeds under existing law that was not designed for the technology.

EXAMPLE — the smoke detector wired to one room. Bill C-16 covers the kitchen, where most of the fires start: sexual deepfakes, the most-documented harm. Political fakes, fake endorsements, and scam videos wearing a trusted face are the rest of the house, left to defamation and election law that were never built for synthetic media. Real protection where the evidence says it's needed most, and bare wiring everywhere else.

What existing Canadian law might apply to political deepfakes, with the gaps:

Defamation. Canadian defamation law could in principle apply to deepfakes that depict identifiable individuals saying or doing things that damage reputation. The challenges: defamation litigation is slow, expensive, requires the victim to bring civil action, and produces remedies (damages) that don't reach the underlying viral content. Anonymous or pseudonymous creators are difficult to identify. The platform hosting the content has limited liability under section 230-equivalent frameworks. Defamation is a real but limited remedy.

The Canada Elections Act. The Act includes provisions against making false statements about candidates that affect voting outcomes (section 91), but the provisions have a high bar for prosecution and are designed for traditional disinformation rather than synthetic media specifically. Elections Canada has issued guidance on AI-generated content but does not have specific deepfake enforcement authority.

Privacy law. Provincial privacy laws and the federal PIPEDA could in principle apply to non-consensual use of a person's image, but the application to synthetic-image cases is unsettled and varies by jurisdiction.

Criminal Code provisions other than Section 162.1. Identity theft, fraud, criminal harassment, and uttering threats provisions could apply in specific cases but are not designed for the bulk-deployment scenarios deepfake-generation enables.

Common-law tort. Various torts (misappropriation of personality, intrusion upon seclusion, intentional infliction of emotional distress) could apply but require costly litigation and produce limited remedies.

What's missing from the Canadian framework that peer jurisdictions have:

  • A specific criminal prohibition on non-consensual deepfakes generally (not limited to sexual content). This exists in Australia, the UK, and various US states.
  • A broader civil layer. Bill C-16's May 2026 amendment added the 48-hour platform-removal duty itself, bringing Canada in line with the US TAKE IT DOWN Act, the EU's notice-and-action obligations (the AI Act and Digital Services Act), and Australia on that specific point. What Canada still lacks is the wider civil mechanism (victim-initiated statutory damages and a standing regulator) that lets someone act without waiting on either a criminal charge or a platform's compliance.
  • Significant financial penalties for non-compliance that create real platform-side incentives. The UK Online Safety Act allows fines up to £18 million or 10% of global annual revenue. Australia's framework includes civil penalties up to AU$782,500 per violation. Canada has no equivalent for non-sexual deepfakes.
  • Mandatory disclosure of AI-generated content in certain contexts. The EU AI Act requires disclosure that content is AI-generated in many use cases. The US has state-level requirements (California, Texas) for political AI disclosure. Canada has no general AI content disclosure requirement.
  • Specific electoral-deepfake provisions. Several US states have passed political deepfake laws specifically targeting the electoral context (California's AB 2839 (2024), successor to the sunset AB 730 — though AB 2839 was preliminarily enjoined as unconstitutional in Kohls v. Bonta (2024), so its enforceability is contested; Texas SB 751; others). Canada has no equivalent.

The structural picture. Canada's deepfake regulation is among the narrowest of the peer jurisdictions this guide surveys: covering sexualized content through criminal law and essentially leaving everything else to existing general legal mechanisms not designed for the technology. This is the regulatory framework the AI for All strategy implicitly accepts, since the strategy does not commit to broader deepfake legislation.

A specific finding that connects to the Chapter 5 leaked draft analysis: the CBC obtained version of AI for All contained more detailed engagement with deepfake-related concerns than the final announcement. The narrowing between draft and announcement reflects the same pattern visible across the strategy: internal candour about challenges, external presentation focused on commitments. The deepfake regulatory gap is one of the specific cases where the gap between internal recognition and external commitment is observable.

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Quebec leadership returning

A pattern this guide has named in Chapter 5, Chapter 9, and Chapter 10 surfaces again: on AI-adjacent policy areas where the federal government has been silent or slow, Quebec has consistently moved earlier and more comprehensively. The pattern holds for deepfakes specifically.

Quebec's Act respecting the rights of action against persons distributing intimate images (officially in force in Quebec in 2024) establishes a civil regime for non-consensual sharing of intimate images, including AI-generated synthetic images, that is distinct from and complementary to federal criminal law. The Quebec regime provides:

  • A civil cause of action allowing victims to seek remedies without requiring criminal prosecution
  • Statutory damages that can be awarded without proving specific harm, providing a remedy in cases where actual damages are difficult to quantify
  • Penalties of up to CA$5,000 per day for individuals and CA$50,000 per day for legal persons (companies, platforms) that fail to comply with takedown orders
  • A faster procedural pathway than ordinary civil litigation, designed to address the time-sensitivity of online content
  • Specific provisions for platform liability that go beyond what federal law provides

The Quebec framework is not perfect. Implementation has been gradual, awareness among potential users is limited, and the regime's scope is narrower than what fully comprehensive deepfake regulation would address. But it represents the most significant Canadian deepfake response currently in force for the specific category of non-consensual intimate images.

The pattern's broader implication. For Canadians outside Quebec, the deepfake regulatory landscape consists primarily of Bill C-16 (criminal law, federal) plus whatever general legal remedies might apply. Quebec residents have additional civil mechanisms with specific deepfake-context provisions, faster procedural pathways, and substantial financial penalties for non-compliance. This 23%-of-population coverage is the working baseline for what Canadian deepfake regulation can look like. The federal level has chosen not to match it.

Whether the federal level should match it is contested: federalist considerations, regulatory burden arguments, and the broader question of whether Canada should pursue comprehensive online-harms legislation or stay with the narrower Bill C-16 approach all factor in. That Quebec leads here, and that the gap matters for Canadians outside Quebec, is a matter of record. Whether the federal government should close it is a contested policy choice, and the guide presents it without resolving it.

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The international comparison framework

Putting Canada's position in the comparative context provides the structural picture readers need to evaluate the regulatory choices:

United States. The TAKE IT DOWN Act (Tools to Address Known Exploitation by Immobilizing Technological Deepfakes On Websites and Networks Act, signed into law May 2025) requires platforms to remove non-consensual intimate images, including AI-generated, within 48 hours of notification. Civil and criminal penalties for non-compliance. Multiple state-level laws including California AB 2839 (2024; political deepfake prohibitions around elections, successor to the sunset AB 730 — though AB 2839 was preliminarily enjoined on First Amendment grounds in Kohls v. Bonta (2024), so its enforceability is contested), Texas SB 751 (deepfake fraud provisions), Tennessee ELVIS Act (likeness protection). State frameworks vary substantially; federal coverage is partial and growing.

United Kingdom. The Online Safety Act (in force 2023, with implementation phasing through 2025–2026) establishes comprehensive online-harms regulation including deepfake provisions. Ofcom is the regulator. Fines up to £18 million or 10% of global annual revenue. Specific provisions for non-consensual intimate images, harm to children, and various other categories. The Act has been controversial in implementation (free-speech concerns, encryption disputes) but represents the most comprehensive single-jurisdiction online-harms framework currently in force in a Westminster-system democracy.

Australia. The eSafety Commissioner has specific authority over online safety including deepfakes. The framework includes 2024 amendments specifically addressing AI-generated content. Civil penalties up to AU$782,500 per violation. Criminal penalties of up to six years' imprisonment (seven in aggravated cases) for sharing non-consensual sexually explicit deepfakes, under the 2024 federal amendments. Comprehensive scope including children's safety, image-based abuse, and harassment.

European Union. The AI Act (effective August 2024, with phased implementation) establishes risk-based AI regulation including transparency requirements for AI-generated content. The Digital Services Act (effective 2024) establishes notice-and-action obligations for online platforms. Combined, these provide the most comprehensive regulatory framework for AI-generated content globally. Significant fines (up to 7% of global annual turnover under the AI Act for the most serious violations; up to 6% of worldwide turnover under the DSA).

Singapore. Substantial framework including the Protection from Online Falsehoods and Manipulation Act (POFMA) and complementary legislation specifically addressing AI-generated content.

South Korea. Specific legal framework including criminal provisions for deepfakes with substantial penalties.

Japan. Active regulatory engagement including AI-content-disclosure proposals.

Canada. Bill C-16 covers sexualized deepfakes under criminal law and, as of the May 2026 amendments, adds a 48-hour platform takedown duty; it is federal. Quebec adds a civil regime for intimate images, but only within Quebec. This is still among the narrowest frameworks in the peer group surveyed here (US, UK, Australia, EU, Singapore, South Korea, Japan). Canada has neither comprehensive online-harms legislation (the Online Harms Act died), nor specific political deepfake provisions, nor mandatory AI content disclosure, and, pending confirmation of how the new takedown duty is enforced, no mature platform-side civil-penalty regime comparable to its peers.

The comparison is not an argument that Canada should adopt any specific peer country's framework. Each framework reflects specific political contexts and trade-offs. The comparison is an honest mapping of where Canada sits in international peer comparison: substantially behind on coverage breadth, with the gap concentrated in political-deepfake regulation, platform-side civil enforcement, and AI-content disclosure. Whether this gap should be closed, and through what mechanism, is contested policy. That it exists is not.

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The convergent likeness rights mechanism

Chapter 11 introduced a specific finding: ACTRA's NIL Rights argument (creator-rights framing) and CIGI's likeness-rights-in-copyright argument (privacy-and-harm-prevention framing) converge on the same legislative mechanism — the recognized likeness right defined in Chapter 11. This convergent mechanism is the most concrete policy path the guide has identified that engages both the creator-rights debate from Chapter 11 and the deepfake regulation gap from this chapter.

Working through how a likeness rights framework would address the gaps:

For sexualized deepfakes. Bill C-16's criminal provisions provide one remedy. A civil likeness rights framework would provide an additional, faster, victim-driven remedy — the victim wouldn't need to wait for criminal prosecution, could bring civil action directly, and could pursue statutory damages without needing to prove specific economic harm. This addresses the procedural gap in current Canadian law for sexualized deepfakes.

For political deepfakes. The current Canadian framework has essentially no specific remedy for non-sexualized political deepfakes (a fake video of a politician saying something they didn't say, for example). A likeness rights framework would provide a remedy: the politician (or any depicted individual) would have a claim against the unauthorized commercial use of their likeness, regardless of the specific content. This addresses the political deepfake regulatory gap directly.

For commercial deepfakes. The current framework relies on defamation, common-law misappropriation of personality, and contract-based protections (which ACTRA's collective agreements exemplify) — a remedy scattered across several legal mechanisms. A likeness rights framework would consolidate it into one generalized remedy applicable across the workforce and the population.

For identity-fraud deepfakes. The recent Carney scam-deepfake case illustrates a category where current law is fragmented — criminal fraud applies to the scam, but the deepfake itself sits in the gap between criminal and civil remedies. A likeness rights framework would provide the depicted individual with a direct civil cause of action against unauthorized commercial use of their likeness, complementary to whatever criminal action follows the underlying scam.

The Danish model that CIGI specifically cites. Denmark's proposed Copyright Act amendment would establish likeness rights as part of copyright law: a new section giving individuals exclusive rights over realistic digital imitations of their appearance and voice, protection running fifty years past death, with explicit carve-outs for caricature, satire, parody, and criticism of power. The amendment was put out for consultation in mid-2025, notified to the European Commission that October, and still awaits final passage in the Folketing as of mid-2026. The right to one's image, voice, and likeness joins the same legal framework that protects creative works. Enforcement uses the same mechanisms (takedown orders, statutory damages, injunctive relief) that copyright enforcement uses. The model has the advantage of building on existing infrastructure rather than requiring an entirely new regulatory regime.

The convergent mechanism's significance for Canadian policy. A single piece of Canadian legislation could close substantial portions of the Chapter 11 IP/copyright gap and the Chapter 13 deepfake gap simultaneously. It would not satisfy every position — TWUC and ACTRA would still pursue compensation frameworks beyond what likeness rights provide, civil liberties advocates would scrutinize the scope of restrictions on synthetic content, and Geist/Craig might worry about overextension into legitimate satirical and educational use. But it would advance both debates meaningfully through a single legislative vehicle. The federal AI strategy could include this commitment. AI for All does not currently include it. Chapter 20 returns to the question of how it might.

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The working test

Same standing test, deepfake edition — in brief, because you've run this drill five chapters in a row. For any deepfake claim, ask: category (sexual, political, commercial, identity-fraud, satirical — the category determines both the harm analysis and which remedy, if any, exists); scope (empirical study, high-profile anecdote, or speculative projection?); reach (the 2025 study's 0.12%-of-views (on X) finding for the harmful subset is the kind of number to demand before accepting a harm claim); remedy (Bill C-16 for sexual content; defamation if you have years and money; Quebec's civil regime if you live there; likeness rights if Parliament ever enacts them); verification (who funded the research, and what was the methodology?); and politics (alarm and dismissal both have beneficiaries — the roughly-2× partisan asymmetry is the rare finding that resists capture by either side).

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What the evidence actually licenses

CHAPTER RECAP — you now have: - Bill C-16's deepfake provisions in operational detail — the Section 162.1 amendments, the "nearly nude" expansion, the 10-year maximum, the new threat-to-disclose offence, the youth-involvement provision, and the 48-hour platform takedown duty added in May 2026. And the bill's deliberate narrow scope — sexual deepfakes only, a criminal core plus a platform-takedown duty but no broad civil-damages regime. - The 2025 Canadian election empirical study — 5.9% deepfake rate, 8.66% on right-leaning accounts vs 4.42% on left-leaning, mostly benign content, 0.12% of X views for the harmful subset, with the Mark Carney scam-deepfakes and DFRLab's Meta-news-block structural finding as the specific documented cases. - The political-deepfake regulatory gap mapped honestly — defamation, Canada Elections Act, privacy law, criminal provisions, common-law tort, all available but none designed for the technology, none providing the comprehensive operational remedy that peer jurisdictions have established. - Quebec's deepfake civil regime as the leading Canadian framework currently in force — CA$5,000/day individual and CA$50,000/day legal-person penalties, with the Quebec-leads-Ottawa-follows pattern returning for the third or fourth time. - The international comparison framework — Canada as among the narrowest deepfake regulatory frameworks in the surveyed peer group, with specific gaps in political coverage, platform-side civil enforcement, and AI-content disclosure relative to the US, UK, Australia, EU, Singapore, South Korea, and Japan. - The convergent likeness rights mechanism — ACTRA's NIL Rights and CIGI's likeness-rights-in-copyright converging on the Danish-model framework that could close substantial portions of both the Chapter 11 IP gap and the Chapter 13 deepfake gap through a single legislative vehicle. - The working test for evaluating any deepfake-related claim: category, scope, reach, remedy, verification, politics.

The next chapter (Chapter 14) turns from the synthetic content flooding the feed to the institution that is supposed to catch it: the news. The deepfakes and scams in this chapter land on an information system already under severe financial strain, with AI arriving inside the newsroom and outside it at the same time. Chapter 14 follows the money — who still pays to find out what is true, and what happens to public knowledge when that funding collapses while synthetic "news" becomes free to produce.

You can now read any Canadian deepfake-related claim with the empirical grounding to recognize what the documented landscape actually looks like: real but limited, structurally gendered in some ways and partisan-asymmetric in others, and regulated through one of the narrowest frameworks among comparable democracies. The deepfake conversation in Canadian public discourse oscillates between alarm and dismissal. The empirical evidence sits in the middle, with specific findings that policy can engage and specific gaps that policy could close.

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Bias label for this chapter: empirical-and-regulatory analysis of Canadian deepfake landscape, with specific commitment to presenting the 2025 election study findings in their nuanced framing rather than collapsing them toward either alarm or dismissal. Author lean: skeptical of both "deepfakes broke the election" and "deepfakes don't matter" framings; sympathetic to the convergent likeness rights mechanism as a path through current regulatory fragmentation; willing to name the structural gap between Canadian framework and peer-country frameworks as the empirically dominant feature of the current situation; explicit about the limits of empirical evidence from a single election in projecting future deepfake-deployment patterns. Government framing (Bill C-16, ministerial statements) treated as primary on legislative commitments. Academic-empirical sources (the 2025 election study, DFRLab analysis) treated as primary on factual claims about prevalence and reach. Civil society documentation (CIGI on likeness rights, ACTRA on NIL Rights) treated as primary on policy positions. International peer-country frameworks treated as primary on legislative content.

Primary sources cited or relied on in this chapter: Bill C-16, the Protecting Victims Act (introduced December 9, 2025, Justice Minister Sean Fraser; amended May 2026, advancing through Parliament as of June 2026); Global News and CBC reporting on the January 2026 Grok deepfake scandal and the Office of the Privacy Commissioner's investigation of X (2026); Canadian Criminal Code Section 162.1 (as amended); "Deepfakes in the 2025 Canadian Election: Prevalence, Partisanship, and Platform Dynamics" (ACM Web Conference 2026 / arxiv:2512.13915); Livernoche et al. (2025) OpenFake dataset documentation; Canadian Digital Media Research Network and Digital Forensic Research Lab analyses of the 2025 Canadian federal election; SITE Task Force public testimony post-election (Nathalie Drouin, David Morrison); Quebec Act respecting non-consensual sharing of intimate images (2024); Centre for International Governance Innovation analysis "Canada must do more to protect women and girls from harmful deepfakes" (December 2025); Suzie Dunn (Dalhousie University) academic work on non-consensual synthetic intimate images; US TAKE IT DOWN Act (2025); UK Online Safety Act (2023, in force 2023–2026); Australian eSafety Commissioner framework; European Union AI Act (effective August 2024); EU Digital Services Act (effective 2024); Denmark's likeness-rights Copyright Act amendment (introduced 2025). Detailed citations in the Sources appendix.

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🧌 GOBLIN CHECK — a personal one. The About the Author section promised this chapter would return to Strange Harvest, so here it is, receipts and all. Our film's festival cut included roughly thirty seconds of licensed, disclosed AI-generated stills used as placeholders during the festival run. The backlash was immediate, so we pulled the material for the theatrical release. Here's the part that belongs in this chapter: the accusations didn't stop — they migrated. Commenters stayed certain, now pointing at sequences that were entirely practical, or at compositing that was merely budget-priced. The lesson the study above documents at scale, I got to live at retail: detection panic doesn't track what's actually synthetic. A label, once applied, doesn't come off. And the audience most confident it could "always tell" performed, on my film, no better than a coin flip. So when you read the 5.9% prevalence number, hold the other side of the coin too: a public that can't reliably spot the 5.9% also can't reliably clear the other 94.1%. The harm isn't only fake content believed. It's real work disbelieved. — A.Y.

Recap

  • Bill C-16's deepfake provisions in operational detail — the Section 162.1 amendments, the "nearly nude" expansion, the 10-year maximum, the new threat-to-disclose offence, the youth-involvement provision, and the 48-hour platform takedown duty added in May 2026. And the bill's deliberate narrow scope — sexual deepfakes only, a criminal core plus a platform-takedown duty but no broad civil-damages regime.
  • The 2025 Canadian election empirical study — 5.9% deepfake rate, 8.66% on right-leaning accounts vs 4.42% on left-leaning, mostly benign content, 0.12% of X views for the harmful subset, with the Mark Carney scam-deepfakes and DFRLab's Meta-news-block structural finding as the specific documented cases.
  • The political-deepfake regulatory gap mapped honestly — defamation, Canada Elections Act, privacy law, criminal provisions, common-law tort, all available but none designed for the technology, none providing the comprehensive operational remedy that peer jurisdictions have established.
  • Quebec's deepfake civil regime as the leading Canadian framework currently in force — CA$5,000/day individual and CA$50,000/day legal-person penalties, with the Quebec-leads-Ottawa-follows pattern returning for the third or fourth time.
  • The international comparison framework — Canada as among the narrowest deepfake regulatory frameworks in the surveyed peer group, with specific gaps in political coverage, platform-side civil enforcement, and AI-content disclosure relative to the US, UK, Australia, EU, Singapore, South Korea, and Japan.
  • The convergent likeness rights mechanism — ACTRA's NIL Rights and CIGI's likeness-rights-in-copyright converging on the Danish-model framework that could close substantial portions of both the Chapter 11 IP gap and the Chapter 13 deepfake gap through a single legislative vehicle.
  • The working test for evaluating any deepfake-related claim: category, scope, reach, remedy, verification, politics.

Sources

  • Bill C-16, the Protecting Victims Act (introduced December 9, 2025, Justice Minister Sean Fraser
  • amended May 2026, advancing through Parliament as of June 2026)
  • Global News and CBC reporting on the January 2026 Grok deepfake scandal and the Office of the Privacy Commissioner's investigation of X (2026)
  • Canadian Criminal Code Section 162.1 (as amended)
  • "Deepfakes in the 2025 Canadian Election: Prevalence, Partisanship, and Platform Dynamics" (ACM Web Conference 2026 / arxiv:2512.13915)
  • Livernoche et al. (2025) OpenFake dataset documentation
  • Canadian Digital Media Research Network and Digital Forensic Research Lab analyses of the 2025 Canadian federal election
  • SITE Task Force public testimony post-election (Nathalie Drouin, David Morrison)
  • Quebec Act respecting non-consensual sharing of intimate images (2024)
  • Centre for International Governance Innovation analysis "Canada must do more to protect women and girls from harmful deepfakes" (December 2025)
  • Suzie Dunn (Dalhousie University) academic work on non-consensual synthetic intimate images
  • US TAKE IT DOWN Act (2025)
  • UK Online Safety Act (2023, in force 2023–2026)
  • Australian eSafety Commissioner framework
  • European Union AI Act (effective August 2024)
  • EU Digital Services Act (effective 2024)
  • Denmark's likeness-rights Copyright Act amendment (introduced 2025).