# Deepfakes and disinformation: the downside of generative AI [Skip to content](#lm-inhoud)Network/[NL](/en/deepfakes-desinformatie)EN[Hubhub.llmnet.nlCompare models on task, language, cost and license.](https://hub.llmnet.nl/en/)[Communitycommunity.llmnet.nlPrompt techniques, patterns and system prompts.](https://community.llmnet.nl/en/)[APIapi.llmnet.nlLLMs in production: rate limits, routing, structured output.](https://api.llmnet.nl/en/)[Consultancyconsultancy.llmnet.nlRolling out AI in an organization, pilot to production.](https://consultancy.llmnet.nl/en/)[Newsnieuws.llmnet.nlAI developments, explained for the Netherlands.](https://nieuws.llmnet.nl/en/)[Benchmarkbenchmark.llmnet.nlMeasure AI quality yourself, on your own tasks.](https://benchmark.llmnet.nl/en/)[Careersvacatures.llmnet.nlAI roles, salaries and career paths in the Netherlands.](https://vacatures.llmnet.nl/en/)[Learnleren.llmnet.nlAI concepts in plain language, beginner to builder.](https://leren.llmnet.nl/en/)[Guidegids.llmnet.nlRun AI privately on your own Mac, PC, NAS or home server.](https://gids.llmnet.nl/en/)[Directorydirectory.llmnet.nlMapping the AI ecosystem: tools, models, companies.](https://directory.llmnet.nl/en/)[Radarradar.llmnet.nlSignals from X, research and communities for indie developers.](https://radar.llmnet.nl/en/)[Appsapps.llmnet.nlReviews of AI apps and open-source repos, with tips for builders.](https://apps.llmnet.nl/en/)[llmnet.nl — main site](https://llmnet.nl/en/)[](https://x.com/intent/post?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fdeepfakes-desinformatie&text=Deepfakes%20and%20disinformation%3A%20the%20downside%20of%20generative%20AI)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fdeepfakes-desinformatie)[](https://www.reddit.com/submit?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fdeepfakes-desinformatie&title=Deepfakes%20and%20disinformation%3A%20the%20downside%20of%20generative%20AI)[](#)[](https://x.com/intent/post?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fdeepfakes-desinformatie&text=Deepfakes%20and%20disinformation%3A%20the%20downside%20of%20generative%20AI)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fdeepfakes-desinformatie)[](https://www.reddit.com/submit?url=https%3A%2F%2Fnieuws.llmnet.nl%2Fen%2Fdeepfakes-desinformatie&title=Deepfakes%20and%20disinformation%3A%20the%20downside%20of%20generative%20AI)[](#)By Ivo Donker — created with AI assistance (Claude & Gemini) · Last updated: July 27, 2026 LLMNet News & Research # Deepfakes and disinformation: the downside of generative AI Published on July 25, 2026 • Reading time: approx. 6 minutes Generative artificial intelligence has reached the mainstream at a rapid pace. While text models and image generators enrich creative processes, they simultaneously bring complex societal challenges. Deepfakes — hyper-realistic manipulated videos, audio, and images — pose an ever-increasing threat to information provision and public trust. ## How generative AI and deepfakes work The term 'deepfake' is a portmanteau of deep learning and fake. The technology relies heavily on neural networks, in particular Generative Adversarial Networks (GANs) and modern transformer architectures. In GANs, two algorithms compete with each other: a generator that creates synthetic media and a discriminator that tries to distinguish between real and fake. Through this continuous struggle, the generator perfects the result until the output is virtually indistinguishable from the real thing. While training such models was once exclusively reserved for large research institutes with massive computing power, the barrier to entry has been drastically lowered in recent years. Consumer hardware and open-source frameworks enable almost anyone to clone voices or swap faces in existing video footage within minutes. ## The societal risks The rise of advanced synthetic media brings various risks for both individuals and democratic institutions: - Political influence and election interference: Fake audio clips or videos of politicians making controversial statements can be spread just before elections to manipulate public opinion before a factual rebuttal is possible. - Reputational damage and fraud: CEO fraud via cloned voices (vishing) and non-consensual pornographic deepfakes cause direct and profound suffering to individual victims. - The 'liar's dividend': A creeping side effect is that politicians and those in power can now easily dismiss real, incriminating recordings as 'a deepfake', putting any objective truth-finding under pressure to be verified. Background: Scientists and sociologists warn that the greatest damage of disinformation does not even lie in the belief in the lie itself, but in the general erosion of trust in traditional media and institutions. ## How do you recognize fake content? Although generative models are constantly improving, AI-generated content often still exhibits specific artifacts. People and systems can look out for the following signals: - Visual anomalies: Look for unnatural lighting, shadows that do not match the environment, asymmetrical earrings, strange hand shapes, or blurry edges around the face and hair. - Audio irregularities: Cloned voices sometimes lack natural breathing, exhibit robotic tone shifts, or have abrupt pauses in cadence. - Contextual verification: Always check if reputable news sources report a shocking statement or event. If a video cannot be found anywhere else, skepticism is warranted. ## What is being done about it? Governments, tech companies, and researchers are joining forces to combat abuse. The European Union has introduced strict transparency obligations with the Artificial Intelligence Act (AI Act), requiring platforms to clearly label AI-generated content. In addition, significant investments are being made in detection tools. Cryptographic watermarking (such as C2PA standards) and AI scanners that analyze pixel patterns and video frequencies should help to automatically flag synthetic media to be verified. Still, it remains an ongoing cat-and-mouse game between detection software developers and malicious actors. Want to know more about the broader foundations of language models and artificial intelligence? Consult our comprehensive [LLMNet AI Guide](https://gids.llmnet.nl/en/) for in-depth technical backgrounds and glossaries. © 2026 LLMNet. All rights reserved. Part of the LLMNet publishing network.