How to Detect Deepfake? 10 Best Practices
Deepfakes, a kind of AI-generated synthetic media, are increasingly being used for fraud, identity theft, and political misinformation. These videos or voices that are hyper-realistic can be used to deceive people and organizations. Therefore, detection is important for security, compliance and public trust. In this blog post, we’ll talk about what to watch out for, and how to protect people from being deceived by deepfakes.
1. Unnatural Eye Movements
One of the most common giveaways of a deepfake video is the unnatural eye movement. Since human eyes move fluidly and blink at irregular intervals, which are more realistic, AI-generated videos fail to capture these nuances. In many deepfake videos, the eyes are open for too long, or blinks are too frequent or mechanical, or they fail to track objects or people successfully. Also, reflections and light sources in the eyes may look mismatched or missing completely. These irregularities in gaze direction, lighting or timing can clue people in regarding deepfakes.
2. Facial Expressions
Deepfake videos struggle to replicate the complexity of real human facial expressions. Real faces display quick and involuntary movements that convey emotion, but AI-generated faces’ facial expressions may look too smooth, rigid, or perfectly lit. Smiles can be too wide or disconnected from the eyes, and emotional reaction may struggle to sync with the tone of voice or context of the video. One other sign can be overly uniform skin texture and lighting inconsistencies across the face. The video is probably fake if expressions feel off or fail to align with speech and emotion.
3. Do Lip Movements Match the Voice?
In deepfake videos, the sync between lip movements and speech is often problematic. The mouth may open or close too early or too late compared to the audio. This unnatural rhythm may clue people in. Minor issues like mismatched jaw motion, missing consonant sounds, or lips that don’t seem to be fully forming words can be signs of deepfakes. Also, deepfakes struggle more with fash speech, complex words, and changes in emotion, with the lip movement not fully matching. The voice may sound natural but the lip motion should also be considered to count the video as a deepfake.
4. Glitches in the Background
One of the most noticeable giveaways of a deepfake is the glitches in background details. Deepfake algorithms prioritize the face often, which leaves edges and surrounding areas imperfect. So, hair may flicker or blend unnaturally into the background, and objects close to the person like earrings, glasses, or shadows may appear distorted. You can also notice shifting outlines, inconsistent lighting, or background elements that seem to melt during movement.
5. Voice and Tone
Audio deepfakes are found out through unnatural sound quality and inconsistencies in speech patterns. The voice in the deepfake may sound overly smooth, monotone and robotic, lacking the natural sounding fluctuations of human conversation. Readers should pay close attention to mismatched intonation, pauses that seem unnatural, and words that seem slightly out of sync with the speaker’s mouth movements. Pronunciation errors or emotional tones that don’t match the context, like a person laughing while saying something serious, can also signal deepfakes.
According to Pindrop’s 2025 report, deepfake-fraud in contact centers surged 1,300% and projected deepfake-related fraud exposure could reach US$44.5 billion. Voice-based deepfake fraud is a serious problem that readers as well as companies should take seriously as well.
6. Is Metadata Missing?
Metadata is there to give important clues about a file’s authenticity, including details like timestamps, device type, and editing history. Deepfake creators often alter or strip this information completely to hide manipulation traces. If a photo or video lacks EXIF data entirely or shows inconsistencies like impossible timestamps or software tags different from the device used, it may be a deepfake.
7. Deepfake Detection Tool
There are many AI-powered tools in the market to help identify manipulated media. Microsoft Video Authenticator is one example that analyzes the video or photo for pixel-level variations to detect deepfake alterations. Deepware Scanner is another alternative that allows users to upload or scan media for signs of synthetic manipulation using deep learning algorithms. Also, Hive Moderation integrates into platforms so that it can automatically flag deepfakes in real-time. These tools can be used together to make sure deepfake detection occurs without flaws.
8. Is the Source Credible?
Before sharing a video or image, or trusting any information it provides, readers should be verifying where it came from. Deepfakes are often shared using unverified social media accounts or freshly created websites for the sole reason of misleading viewers. Checking whether the content comes from a reputable news outlet, verified account, or official organization may be useful. Reviewing publication dates, cross-referencing with credible sources, and using reverse image searches also help immensely during the process of confirming authenticity.
9. Inconsistencies Between Frames
Deepfakes can be uncovered through minor frame-by-frame inconsistencies. Sudden changes in lighting, facial proportions, or skin tone between following frames can clue viewers in. Features like ears, earrings or harlines can also shift in an unnatural manner if the deepfake model is struggling with maintaining alignment. Slowing down the video or pausing it can help reader discover these glitches more easily.
10. Tools Flagging It
Trusted fact-checking platforms like FactCheck.org, Snopes, or Reuters Fact Check are tasked with actively monitoring and exposing deepfake content that’s popular online. These companies use both human verification and AI-powered tools to analyze videos, trace original sources, and compare them with verified data. Users can consult these platforms before blindly believing or sharing any suspicious footage.
Why Is Deepfake Detection Important?
Deepfake detection is important for protecting people, businesses, and national interests. One of the biggest risks is fraud, since deepfakes can be used to impersonate executives of companies or customers to authorize illegal transactions. Reputational damage is another important concern, since these manipulated videos can ruin an entity’s credibility or a person’s life in just a few hours.
In the financial world, deepfakes can be used to trigger market manipulation by spreading false statements which can then later influence stock prices or overall investor confidence. Deepfakes also pose national security threats, since they can be used for disinformation campaigns and political destabilization using falsified evidence.
Many countries are rolling out new laws to protect their citizens from these deepfakes. A bill in Denmark that’s expected to pass early 2026 is aiming change copyright law by imposing a ban on the sharing of deepfakes to protect citizens’ personal characteristics, like their appearance or voice, from being imitated and shared online without their consent. Steps like these will follow in upcoming years to detect deepfakes and protect people from being scammed or violated.
In another example, Manitoba politicians passed bills aimed at banning election disinformation in November, 2025. The bill introduces fines of up to $20,000 a day for election disinformation, including deepfake videos that use artificial intelligence to create realistic but false images or videos of candidates.
