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DEFIANCE Act: New bill proposed in the US to curb non-consensual AI porn and deepfakes

The bill targets “intimate digital forgeries” depicting an identifiable person without their consent, allowing such persons to collect financial damages from anyone who “knowingly produced or possessed” the forged content with the intent to share it.

US lawmakers on January 30th introduced a new bill that allows victims of AI-generated porn and deepfakes to sue for compensation. This bill comes right after the sudden rise in sexually explicit deepfake images of Taylor Swift that prompted the White House to call for legislation to protect people from AI porn.

Called the Disrupt Explicit Forged Images and Non-Consensual Edits (DEFIANCE) Act of 2024, the bill was introduced by lawmakers Dick Durbin, Lindsey Graham, Amy Klobuchar, and Josh Hawley. It still has a long way to go before it becomes law.

The bill targets “intimate digital forgeries” depicting an identifiable person without their consent, allowing such persons to collect financial damages from anyone who “knowingly produced or possessed” the forged content with the intent to share it. Apart from the financial compensation, the court can order the defendant to cease display of the violating content as well.

The bill defines a digital forgery as: “any intimate visual depiction of an identifiable individual created through the use of software, machine learning, artificial intelligence, or any other computer-generated or technological means, including by adapting, modifying, manipulating, or altering an authentic visual depiction, to appear to a reasonable person to be indistinguishable from an authentic visual depiction of the individual, regardless of whether the visual depiction indicates, through a label or some other form of information published with the visual depiction, that the visual depiction is not authentic.”

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To protect the privacy of the victim, the court may allow the plaintiff to use a pseudonym, requiring the parties to redact the personally identifiable information of the plaintiff, and/or issuing a protective order for purposes of discovery.

Why does this matter

Non-consensual pornography or revenge porn is already a rampant and hard-to-tackle issue, but the use of AI to generate such porn makes the problem much harder to tackle because any person with images or videos online can be targeted thanks to the various deepfake tools available online. While public figures, politicians, and celebrities might be the popular targets, the general population is no safer.

While this bill provides legal recourse for victims of AI porn, it puts the burden on the victim to identify and file a suit against the perpetrator. In many cases, identifying the originator of the content might prove to be hard and going after all online persons who reshare the content might be too much. For instance, it is still not clear who generated the deepfakes of Taylor Swift that populated X and yet multiple people on X reshared the same. Who should the popular singer file a lawsuit against in this case? Additionally, before the lawsuit is filed, the damage might already be done with the content going viral on social media.

We need more regulation that proactively curbs deepfake imagery; regulations that go after deepfake generation tools and social media platforms. However, this is easier said than done. For example, X tried to temporarily block searches of Taylor Swift altogether, but people could easily find ways around it.

The challenge with deepfakes extends beyond their easy creation and distribution; identifying and eliminating them also presents significant difficulties. For instance, one popular method for removing child sexual abuse material (CSAM) online is by maintaining something called “hash banks”— every upload is cross-checked with this hash bank to identify any content that matches with what is contained there. But this method won’t work for deepfakes because the scale of the problem is much larger. Speakers at MediaNama’s Deepfakes and Democracy event discussed at length why detecting deepfakes is a challenging problem, you can read their arguments here.

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