In this guide, we show how to spot face swaps in photos and short video using manual review and AI detection, then test how well AI detection performs.<\/p>\n\n\n\n
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Key Takeaways<\/strong><\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n A deepfake face swap is an AI-generated or manipulated image or video that replaces one person\u2019s face with another\u2019s. The technology uses deep learning techniques to map out and reproduce patterns in facial features, expressions, and movements, a process that makes the end replacement look more convincing. <\/p>\n\n\n\n While face swaps can serve harmless entertainment or creative purposes, bad actors also use them to impersonate individuals, catfish victims<\/a>, or spread misinformation. Deepfakes help fraudsters conceal their true identities and build trust with unwitting targets.\u00a0<\/p>\n\n\n\n Fraudsters use deepfake technology to impersonate trusted individuals through live video calls. They might pose as executives, family members, or authority figures to manipulate victims into surrendering money or sensitive information. <\/p>\n\n\n\n Fraudsters borrow the image of a trusted public figure to exploit the goodwill and trust of their audience. They might promote suspicious or high-risk schemes, such as cryptocurrency platforms, investment scams, or miracle products. <\/p>\n\n\n\n A romance scam involves a fraudster using a fake identity to build a relationship with a target, typically through a dating p<\/a>l<\/a>atform<\/a>. The fraudster exploits the target\u2019s emotions, coaxing them to send money, surrender sensitive emotions, or sign up for fraudulent investment platforms.\u00a0<\/p>\n\n\n\n Deepfake technology can create identity documents that appear convincing enough to bypass KYC checks. Fraudsters use these synthetic documents to create fictitious identities, open fraudulent accounts, and gain unauthorized access to financial services.<\/p>\n\n\n\n Although deepfake face swaps are becoming increasingly convincing, visual analysis, metadata inspection, and AI detection tools can still help distinguish them from genuine media. Here\u2019s a step-by-step guide to identifying deepfake face swaps. <\/p>\n\n\n\n Because generative models use mathematical processes to create rather than shooting scenes directly through cameras, their outputs often contain subtle AI artifacts<\/a> that conflict with natural anatomy, lighting, or physical interactions.<\/p>\n\n\n\n Artifacts you can check for include: <\/p>\n\n\n\n This method is effective for older or simpler models, which are not up-to-date with modern generation techniques. However, many newer models have had enough training to conceal artifacts more effectively, making deepfake face swaps harder to identify through visual inspection alone.<\/p>\n\n\n\n If the suspected deepfake came from an online publication (as opposed to a private image, profile photo, or video call) it\u2019s worth examining the surrounding information. The media\u2019s context and source can reveal inconsistencies that suggest manipulation or misrepresentation.<\/p>\n\n\n\n Here are ways to check:<\/p>\n\n\n\n Being posted by a verified source does not guarantee that a photo or video is real. However, it makes the likelihood of the photo being used as a vehicle for fraud much lower. <\/p>\n\n\n\n Metadata refers to the data in an image or video that describes its characteristics, including how it was created, edited, and processed. Meanwhile, provenance records store information about where a file originated and how it has changed over time. Both provide useful clues about the origins of a suspected deepfake face swap. <\/p>\n\n\n\n Inspecting metadata and provenance records involves the following steps: <\/p>\n\n\n\n Use metadata findings as one source of evidence in a broader investigation. Although inconsistencies warrant scrutiny, metadata alone cannot confirm whether a file is authentic or manipulated. Some deepfakes still contain plausible metadata, while legitimate media can include editing records or lack provenance information entirely.<\/p>\n\n\n\n One efficient way to investigate suspected deepfakes is to run an AI detection analysis. AI detection tools use forensic analysis to scan images and videos for signals commonly associated with generative AI, such as statistical patterns, pixel-level anomalies, metadata inconsistencies, and the visual inconsistencies that the human eye might struggle to catch.<\/p>\n\n\n\n Detection models combine these signals to calculate the likelihood that AI created or altered a piece of content. <\/p>\n\n\n\n To test how effectively AI detectors can catch deepfake face swaps, we ran three image face swaps and two video face swaps through Undetectable AI\u2019s image and video detectors. We created the samples by using free face swap generators to blend unlicensed stock images with pre-existing faceswap templates.<\/p>\n\n\n\n The tools we used were the Undetectable AI image detector and the Undetectable AI video detector. Both are powered by TruthScan and trained on models like Dall-E, ChatGPT, and Gemini.<\/p>\n\n\n\n Image Sample 1<\/p>\n\n\n Detection Score = 3% Real<\/p>\n\n\n Image Sample 2<\/p>\n\n\n Detection Score = 1% Real<\/p>\n\n\n Image Sample 3<\/p>\n\n\n Detection Score = 4% Real<\/p>\n\n\n The Undetectable AI\u2019s AI Image Detector<\/a> scores images on their likelihood of being real, which means the closer a score is to zero, the more likely an image is to be AI-generated. Our samples earned scores of 3%, 1%, and 49% respectively.<\/p>\n\n\n\n As you can see, none of the images contained obvious visual defects, save for sample three, which shows some minor lighting and proportion inconsistencies. The AI detector was able to map out the subtle patterns that indicated that the photos were generated mathematically rather than shot directly through a camera. <\/p>\n\n\n\n Video Sample 1<\/a><\/p>\n\n\n\n Detection Score = 86% AI<\/p>\n\n\n Video Sample 2<\/a><\/p>\n\n\n\n Detection Score = 97% AI<\/p>\n\n\n\n
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\n\n\n\nWhat is a Deepfake Face Swap?<\/h2>\n\n\n\n
Examples of Deepfake Face Swap Scams<\/h2>\n\n\n\n
Video Call Impersonations<\/h3>\n\n\n\n
Fictitious Celebrity Endorsements <\/h3>\n\n\n\n
Romance Scams<\/h3>\n\n\n\n
KYC Bypass <\/h3>\n\n\n\n
How to Spot Deepfake Face Swamps<\/h2>\n\n\n\n
Step One: Run a Manual Visual Check<\/h3>\n\n\n\n
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Step Two: Compare the Suspicious Item with Other Media<\/h3>\n\n\n\n
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Step Three: Inspect Metadata and Provenance<\/h3>\n\n\n\n
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Step Four: Run an AI Detection Analysis<\/h3>\n\n\n\n
Can AI Detection Catch Deepfake Face Swaps? A Test<\/h2>\n\n\n\n
Image Results<\/h3>\n\n\n\n
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<\/picture><\/figure>\n<\/div>\n\n\nVideo Results<\/h3>\n\n\n\n
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