AI Video Detection is now live
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As AI becomes increasingly commonplace in today’s society, so has its use by bad actors looking to manipulate or mislead. Among the most dangerous forms of AI generated content out there right now are “deepfake” videos, which distorts footage to appear genuine. What’s more concerning is that deepfakes can be developed to evade detection from software meant to catch AI-generated video.
To uncover why, organizations must start by understanding how deepfakes are created. That includes the strengths and weaknesses of detection tools, was well as the best methods to determine what is real and what is a deepfake.
A deepfake is media that’s manipulated to make someone appear to say or do something they did not. The majority of deepfakes are created using AI models that are trained on large datasets of real images or footage. Through studying speech patterns, facial expressions, movements, and visual details, AI can generate synthetic media closely resembling real people.
Often, deepfakes are used to generate scandal, target public figures, or defame someone by making the public believe they did or said something they didn’t. There have been instances of political ads, for example, that utilize deepfakes to negatively portray an opponent.
Deepfakes initially gained mainstream attention through static images with easier AI “tells.” But recent advancements in AI video generation have taken what’s possible to another level.
Today’s tools can:
The number of deepfakes skyrocketed from around 500,000 in 2023 to nearly 8 million in 2025, and the amount of deepfake content in general is projected to increase by 900% annually. As tools become more accessible and AI capabilities more advanced, deepfakes have appeared more frequently across messaging apps, social media, political discourse, entertainment content, and political discourse.
Most deepfake videos rely on AI models like generative adversarial networks (GANs), diffusion models, and facial reenactment systems. Typically, AI is trained on footage of a real person to learn expressions, facial structure, and speech patterns. Then, the model generates synthetic content that’s designed to mimic those behaviors. Some deepfakes alter only portions of a video – such as modifying audio or replacing a face – while others generate fully synthetic scenes from scratch.
Not every manipulated video is technically a deepfake. Cheapfakes are created without AI, using lower-tech editing methods like cropped footage, selective editing, misleading captions, slowed or sped-up playback, or re-contextualized video clips. They can still effectively spread misinformation, since the manipulation is contextual instead of synthetic.
Deepfake video detection remains a challenge. Unlike a single image, video brings to the table complexity, including timing, audio synchronization, movement, frame consistency, and compression artifacts. There are additional elements that detection systems have to analyze, which leaves more room for error. In addition, it’s possible for manipulation to exist in only a few frames or isolated sections, rather than throughout the full clip. Some systems may only detect whether AI was involved as a whole, and it’s far more difficult to identify exactly which frames were altered.
There is also the nature of videos themselves, compared to images. Videos uploaded to social platforms are often clipped, resized, compressed, or screen-recorded multiple times, removing subtle details that detection systems often rely on.
Perhaps the biggest issue, though, is that AI capabilities are constantly evolving. Video generation tools are quickly improving, and many newer systems are designed to eliminate the visual inconsistencies that older detection tools once relied on. If detectors aren’t updated continuously, they won’t be able to catch videos generated with the newest models.
Today, there is currently no single industry-standard solution for detecting video deepfakes. Organizations instead are relying on a fragmented mix of forensic analysis tools, human review, and AI-powered detection platforms.
Human reviewers may look for common signs like unnatural blinking, lip-sync mismatches, facial warping, inconsistent lighting, or distorted movement around hairlines or edges. Some teams also extract screenshots from videos and run them through image detection tools, though paused frames lose motion context and often suffer additional quality degradation.
Meanwhile, specialized deepfake detection platforms use techniques including audio analysis, metadata evaluation, frame-by-frame analysis, and facial landmark tracking. Results can vary widely across tools, and the market doesn’t currently have a clear leader or universal standard.
False negatives and false positives also remain a major challenge. Some authentic videos may be incorrectly flagged as manipulated, while sophisticated deepfakes may evade detection entirely. For example, Hive Moderations returns scans as “not likely to contain AI-generated or deepfake content,” even on videos clearly watermarked with AI creation software.
While some detectors can accurately detect AI video, they might still determine its rating only if an entire video is AI-generated, instead of highlighting snippets in an otherwise normal video.
As AI-generated video capabilities evolve at such a rapid pace, detection systems must grow alongside them. Future solutions will likely require more advanced multi-modal analysis that is capable of evaluating audio, video, image consistency, and manipulated segments at the same time. The industry is also moving toward granular analysis that can identify where manipulation occurred rather than simply labeling an entire video as either fake or authentic.
Copyleaks has already developed AI-powered image detection technology that can identify manipulated regions within images, and is currently working on similar capabilities for video. In an environment where synthetic media is becoming harder to distinguish from reality and deepfakes can cause significant harm, organizations need scalable tools that can help identify manipulation and verify authenticity across formats.
Yes — and increasingly so. Many detection tools struggle to keep pace with rapidly advancing video generation models, which are often designed to eliminate the visual inconsistencies that older detectors relied on. Some tools may also only flag a video as fake if it’s entirely AI-generated, missing cases where only isolated frames or segments were manipulated.
Common visual indicators include unnatural blinking, lip-sync mismatches, facial warping, inconsistent lighting, and distorted edges around the hairline. However, as telltale signs become widely known, deepfake creators adapt — for example, early deepfakes often featured people who didn’t blink, but once that became a known red flag, the technique was corrected in newer generations of fakes.
A deepfake uses AI — typically models like GANs or diffusion systems — to generate or manipulate video in a highly realistic way. A cheapfake, by contrast, is created using basic, non-AI editing techniques like cropping, slowing down footage, or recontextualizing a clip. Both can spread misinformation effectively, but deepfakes are significantly harder to detect.
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