Deepfakes, AI Content, and Synthetic Reality
A video shows a politician saying something they never said. An image depicts an event that never happened. A voice clone impersonates someone convincingly. Artificial intelligence has made synthetic media—content generated or manipulated by AI—increasingly realistic and accessible. The implications for trust, truth, and public discourse are profound.
What Synthetic Media Can Do
Deepfakes
Deepfake technology uses machine learning to create realistic video of people saying or doing things they did not actually say or do. Face-swapping, lip-syncing to different audio, and full-body synthesis have all become possible with varying degrees of realism.
Creating convincing deepfakes once required significant expertise and computing power. Tools have become more accessible, putting synthetic video creation within reach of non-experts.
Voice Cloning
AI can clone voices from relatively small samples of audio. A cloned voice can speak any text with the original speaker's tone, accent, and vocal characteristics. Voice cloning threatens voice authentication and enables audio impersonation.
Generated Images
AI image generators like DALL-E, Midjourney, and Stable Diffusion create realistic images from text descriptions. Generated images can depict events that never occurred, people who do not exist, or manipulations of real scenes.
Generated Text
Large language models generate human-like text at scale. AI-generated articles, comments, and messages can flood platforms with content that appears human-authored.
Threats and Harms
Disinformation
Synthetic media enables disinformation at new scales and with new realism. Fabricated evidence for false claims, manufactured statements by public figures, and fake documentation of events that did not occur all become possible.
Even imperfect synthetic media may be convincing enough to spread before detection. By the time fakes are identified, damage may be done.
Non-Consensual Intimate Images
Deepfake technology has been used to create non-consensual intimate images—realistic fake pornography featuring real people without their consent. This has disproportionately targeted women and constitutes a serious form of image-based abuse.
Fraud
Voice cloning enables phone fraud where scammers impersonate known individuals. Family members have been deceived by cloned voices claiming to be relatives in distress.
Erosion of Evidence
As synthetic media becomes more prevalent, authentic evidence becomes easier to dismiss. Public figures can claim genuine recordings are fakes. The "liar's dividend" lets guilty parties deny authentic evidence.
Trust Collapse
If nothing can be trusted as real, public discourse loses its grounding. Shared facts become impossible if any evidence can be dismissed as synthetic.
Detection and Response
Technical Detection
Detection tools look for artifacts—inconsistencies in lighting, unnatural eye movement, audio anomalies—that betray synthetic origin. But detection is an arms race; as detectors improve, generators improve to evade them.
Provenance and Authentication
Content provenance systems—tracking where media originated and how it has been modified—offer alternatives to detecting fakes after the fact. Standards for media authentication are developing but not widely deployed.
Legal Responses
Laws against non-consensual intimate images have expanded to cover deepfakes in some jurisdictions. Broader regulations on synthetic media are being considered but face definitional and enforcement challenges.
Media Literacy
Teaching people to approach media skeptically—questioning sources, looking for verification, being cautious about viral content—provides some defense. But literacy cannot fully counter technology that defeats human perception.
Platform Policies
Social media platforms have policies against synthetic media in certain contexts—election interference, non-consensual intimate images—with varying enforcement effectiveness.
Canadian Context
Canada does not have comprehensive legislation addressing deepfakes, though existing laws on fraud, defamation, harassment, and non-consensual intimate images may apply in some cases. Bill C-27's proposed AI regulations could eventually address some synthetic media concerns.
The Question
If technology can create synthetic media indistinguishable from authentic recordings, then the evidentiary basis for shared truth is threatened. How should society adapt to a world where seeing is no longer believing? What technical, legal, and educational measures can preserve trust in genuine evidence? And how should the harms from synthetic media—particularly non-consensual intimate images—be addressed when the technology is widely accessible?