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A face swap is one type of deepfake, not a synonym for every deepfake. It replaces one person’s face in existing imagery. The broader term “deepfake” also covers altered facial movement, computer-generated faces and synthetic speech. A face morph is different: it blends two people’s facial images into one.
Is a face swap a deepfake?
Usually, yes. A face swap—also called face replacement—puts one person’s face into another person’s image or video. The UK government’s Centre for Data Ethics and Innovation (CDEI) describes face replacement as face swapping in its 2019 paper on deepfakes. The U.S. Government Accountability Office (GAO) likewise uses “deepfake” as a broad term for video, photos or audio that appear real but have been manipulated with AI.
So, if a clip keeps an actor’s body and movements but puts another person’s face on them, it is both a face swap and a deepfake. The reverse is not true: many deepfakes do not swap faces.
What kinds of manipulation count as deepfakes?
The clearest way to distinguish them is to ask what the media changes. CDEI’s typology and the GAO’s overview describe several different targets of manipulation:
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- Face swap or replacement: changes whose face appears in existing footage or an image.
- Face re-enactment: changes a person’s facial movements or expressions—such as mouth, eyes or eyebrows—without necessarily changing their identity. For example, a video might make someone appear to say words they never spoke.
- Face generation: creates a face image rather than inserting an existing person’s face into another person’s footage.
- Speech synthesis: generates or imitates a voice. It can be used on its own or alongside manipulated visuals.
These categories can overlap in one piece of media. A video might combine a face replacement with generated speech, for example. Calling every such technique a “face swap” obscures what was actually altered.
How is a face morph different from a face swap?
A face morph blends facial images of two people into a single synthesized image. It is not simply one person’s face placed on another’s. The distinction matters in identity checks: NIST describes morphing as an identity-fraud risk because the blended image may be accepted as resembling both people by face-recognition systems. NIST’s 2025 guidance on face-photo morphs treats morph detection as its own problem.
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Can you tell if a video is a deepfake?
Sometimes visual or technical clues can raise suspicion, but a quick visual check cannot reliably establish whether a video is authentic. A glitch—or the absence of one—is not proof either way. The GAO’s 2020 overview notes that detection is difficult as manipulation methods evolve; it also cautions that tools cannot reliably complete the entire analysis automatically and need diverse data and regular updating.
Forensic performance depends on the kind of manipulation, media and operating conditions involved. NIST’s Deepfakes 2026 forensics program evaluates detection across manipulations that include face swapping; a result for one tested setting should not be treated as a universal accuracy rate or guarantee.
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When the concern is a face morph
NIST distinguishes two detection situations: single-image detection, when an examiner has only the suspected image, and differential detection, when there is also a known-genuine image for comparison. NIST computer scientist Mei Ngan said in the agency’s 2025 release, “Some modern morph detection algorithms are good enough that they could be useful in detecting morphs in real-world operational situations.” She also said, “Our publication is a set of recommendations that can be tailored to a specific situation.” These comments concern face morphs specifically; they are not a general assurance that deepfakes can be detected.
When media is submitted for remote identity proofing
NIST’s SP 800-63A, Identity Proofing and Enrollment says covered identity-proofing providers should analyze submitted media for manipulation indicators and use technical controls that increase confidence the media came from a genuine sensor. The standard also makes clear that no single set of mechanisms addresses every possible attack. These are controls for identity-proofing processes, not a standalone test that proves a video’s authenticity.
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What else helps assess synthetic media?
Detection is one part of the response. NIST’s 2024 overview of technical approaches to digital content transparency covers content authentication and provenance tracking, labeling and watermarking, synthetic-content detection, and other risk-reduction approaches. Provenance information or a label can inform an assessment, but neither is a universal authenticity guarantee.
In practical terms, treat a detector output, visual clue, watermark or provenance signal as evidence to weigh—not as a verdict on its own. The relevant question is what was changed and whether the available evidence fits the specific media and context.
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