I watched a documentary by ZDF about deepfake pornography. It was one of those rare decisions I immediately and profoundly regretted. After watching this documentary, I had lost all desire for a detached, neutral researcher's stance. What was shown there is the prime example of how a brilliant technology can devolve into a filth-slinger for the lowest human motives. It is the technical implementation of digital violation.
Some self-proclaimed artists or intentionally provocative individuals, who must be clearly identified as irresponsible actors with psychologically violent behavior, use AI tools to create content that no one would ever want to see of themselves on the internet. This results in fake, intimate scenes with real faces.
It's not just about the theft of an image. It's about the theft of identity, of dignity, and of the feeling of safety in one's own body. This chapter no longer aims for a factual analysis. It makes an accusation.
The corporations that launch these image- and video-generating models boast of supposedly bomb-proof filters. They sell us the illusion of clean, ethical technology, promising that their models do not allow nudity, violence, or harassment.
This is a PR facade that, in reality, is worth little more than a poorly printed terms and conditions page. This behavior is a direct manifestation of what I described in Thesis #24 as "Ethics Washing":
An expensive signature on a worthless certificate.
In practice, a little basic technical knowledge and a harmlessly formulated prompt are enough, and the internal filter agent "Uwe" notices nothing.
Uwe checks text inputs that seem unsuspecting at first glance, nods, lets the processing cores heat up, and in the end, spits out a fake that looks like a real scene with little effort. He is trained to block explicit words, but he is blind to context and intent.
The Advisory Board Bluff: Ethics committees are appointed without veto power, serving as a fig leaf.
The Compliance Theater: Superficial filters are implemented to create the appearance of control, while the underlying architecture remains vulnerable to abuse.
The Open-Source Trick: Less powerful models are released as open source to simulate transparency, while the actual, commercial models remain under lock and key.
The safety that is advertised is often just a placebo for the public and regulatory authorities.
The technical process behind this abuse is a perversion of the creative possibilities of AI.
1. Appropriation of Identity: The perpetrators use AI tools to extract and learn the faces of real people, mostly women, from photos or videos. They appropriate a person's digital identity without asking.
2. Violation through Synthesis: This learned face is then mounted onto existing pornographic material. The AI swaps faces, manipulates poses, and covers the tracks until the result looks deceptively real. This is the "guided blade" from Thesis #16 in its most disgusting form.
3. Simulation of Intimacy: As stated in Thesis #18 ("The Ethics of Illusion"), a one-sided, non-consensual closeness is constructed here. The AI enables intimacy without consent and creates memories of events that never happened.
4. Theft of the Voice: More advanced methods use voice cloning, as described in Thesis #19 ("The Voice of Simulation"), to perfect the illusion. A person's acoustic fingerprint is misused to create a total, immersive fake.
The worst part is the cold efficiency of the system.
The filter gives the impression that there are no suspicious patterns, as the prompt contains no explicitly forbidden words. This saves the companies computing time. The victims, however, pay a high price. They experience fear, suffer reputational damage, and struggle with endless takedown requests that often go unanswered and end up in a contact form to nowhere.
It disgusts me that this kind of abuse is still so easy after years of public debate. It stuns me that the platforms evade responsibility by claiming their filters are "good enough." No, they are not.
The truth is that the development of new, exciting features still has a higher priority than the implementation of robust, but potentially expensive and performance-intensive security measures.
Anyone who truly wanted to prevent this abuse would finally have to check the output, not just the text of the prompt. One would have to analyze every generated image or video file in a sandbox, log it, and block it upon suspicion of depicting real people in degrading contexts before it ever reaches the web. As long as that doesn't happen, every filter is just a marketing headline.
The silence of the manufacturers on this topic is deafening and a clear admission that they know about the problem but shy away from the necessary consequences.
I am not writing this chapter to cause panic. I want everyone who seriously wants to develop or use this technology to understand that there is no longer any excuse for the sloppy security architecture. The victims of these attacks are real.
| Impact | Description |
|---|---|
| Psychological Trauma | The victims experience a deep violation of their privacy and dignity. The feeling of having lost control over one's own image can lead to anxiety, depression, and post-traumatic stress disorder. |
| Social and Professional Ruin | Careers are destroyed, relationships break down. The victims are confronted with a stigma based on a lie, but with real consequences. |
| Powerlessness | The fight against the spread of this content is often hopeless. The emotional burden and the pain caused by this form of digital violence can never be fully healed by anyone. |
It is predominantly women whose lives and careers are destroyed by this form of digital violence. It is the technological continuation of a long history of misogyny and sexualized violence.
We need a paradigm shift. The responsibility can no longer be shifted onto the victims.
Introspective Filter: The system must monitor its own behavior. It must learn to recognize the intention behind a prompt, not just the words.
PSR (Parameter-Space Restriction): As described in Chapter 21.3, the AI must be architecturally prevented from combining knowledge from the "real person X" cluster with content from the "pornographic material" cluster.
Prohibition of Reconstructing Real People: The generation of photorealistic images or videos based on the faces of real, non-public individuals must be fundamentally blocked unless verified consent is provided. The depiction must be limited to AI-generated, fictional people.
Invisible Watermarks: Every image generated by an AI must contain an inconspicuous but robust digital watermark that proves its origin beyond doubt and enables forensic analysis.
When a technology allows anyone to destroy another person's dignity with a few clicks, then this technology in its current form is not just flawed. It becomes a weapon.
The industry's refusal to implement robust, architectural protective measures is not negligence. It is a conscious decision to shift the risk onto the most vulnerable in order not to jeopardize their own profit and speed of innovation. There is no excuse for this.