Deepfakes have moved out of research labs and political disinformation and into everyday fraud. A financial controller in Hong Kong was convinced by a realistic AI video conference call — featuring a convincing simulacrum of his company's CFO — to wire $25 million to criminal accounts. Voice clone technology now requires as little as three seconds of audio to replicate someone's voice convincingly enough to fool people who know them well. AI-generated profile photos are used in romance scams, fake job listings, and corporate impersonation. Knowing what to look for is not optional anymore. It is a basic literacy skill for anyone who interacts with digital media.
This article breaks down the specific tells — visual, audio, and behavioral — that AI-generated content leaves behind, and gives you the tools to verify what you're seeing before you act on it.
$25MLost in a single deepfake video-call fraud, Hong KongReuters, Feb 2024 ↗
2022Year the FBI issued its first formal deepfake employment fraud warningFBI IC3 PSA, Jun 2022 ↗
What Is a Deepfake — and Why It Matters Now
A deepfake is any media — image, video, audio, or text — that has been synthetically generated or manipulated using AI to make it appear authentic. The term originally referred specifically to face-swap video technology, but it now covers a spectrum: AI-generated photographs of people who don't exist, video footage with one person's face replaced by another, cloned voices, and hybrid content that combines real and fabricated elements.
The technology has become dramatically easier to use. Several years ago, producing a convincing deepfake video required significant technical skill and compute resources. Today, Sensity AI and other detection organizations track hundreds of commercially available tools capable of generating photorealistic faces, lip-synced video, and voice clones without any specialized knowledge. The barrier is gone. The output quality is not.
Why AI Detection Tools Are Not Enough on Their Own
Automated deepfake detection tools are useful, but they are not definitive. A 2021 research review by MIT Media Lab and the Stanford Internet Observatory documented a persistent "arms race" between generation and detection — as detectors improve, generators adapt. No single tool catches everything. The techniques below teach you what to look for yourself, which is often the fastest and most reliable first check.
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AI-Generated Images — The Visual Tells
Applies to: profile photos, news images, fake ID documents, social media content
AI image generators produce photorealistic results in seconds, but they leave behind consistent patterns of failure. These are the areas to examine first — roughly in order of reliability as tells.
01
Hands. AI notoriously struggles with hands. Look for the wrong number of fingers, fingers that merge together at the base, joints that bend in impossible directions, or palms that appear distorted. Hands are the single most reliable deepfake tell in static images — check them first.
02
Text inside the image. AI image generators almost universally garble embedded text. Words become nonsensical letter-like shapes, and signs or shirts with writing are almost always unreadable. Any image containing legible, accurate text is more likely to be real or manually edited.
03
Ears, earrings, and jewelry. Accessories are frequently asymmetrical — one earring may differ from the other, or a necklace may appear to phase in and out of skin. The ear itself is a complex shape that AI often renders inconsistently between left and right sides.
04
Eyes. AI-generated eyes can have irregular pupil shapes, irises that differ in color or detail between left and right, reflections of light sources that don't match the scene, or a slightly "glassy" quality. The whites of the eyes may have unusual vein patterns or none at all.
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Skin texture. AI faces often show one of two extremes: unnaturally smooth skin with no visible pores (a plastic or porcelain appearance), or artificial texture that looks painted on. Real skin has natural variation — slight redness, pores, occasional imperfection. The absence of all of these is a flag.
06
Teeth. Teeth may be rendered with inconsistent sizing, too-perfect symmetry, or blurring at the edges where they meet the gum line. During any mouth movement, teeth are among the first elements to degrade.
07
Hair. AI hair often looks visually complete but physically impossible — individual strands may vanish into the background at the hairline, unusual clumping may appear, or the hair-to-background boundary is too crisp or too diffuse to be natural.
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Glasses. Frames that don't align properly, lenses that don't reflect the environment plausibly, or temples (the arms) that fade into the side of the head rather than connecting to it. Glasses are geometrically constrained in ways AI has historically struggled with.
09
Background coherence. Objects in the background may be subtly distorted, repeated, or incoherent — especially faces of bystanders, street signs, or regular geometric patterns like brickwork that should be consistent but aren't. AI fills backgrounds probabilistically, not logically.
10
Unnatural symmetry. AI generators have a statistical bias toward symmetrical faces. A face that appears almost perfectly mirror-symmetrical — particularly for someone who is supposed to be a real person — is worth scrutinizing. Real faces, even very attractive ones, have measurable asymmetry.
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Deepfake Video — Motion Artifacts and Temporal Tells
Applies to: news clips, video calls, political content, corporate impersonation
Video deepfakes must maintain consistency across every frame — a much harder problem than generating a single image. That computational challenge is where the tells concentrate.
01
Face boundary blur. The edge where the generated face meets the neck, hairline, or background is where compositing artifacts are most visible. Look for a subtle "halo" effect, inconsistent sharpness, or a slight color fringing around the face perimeter — particularly when the subject moves.
02
Unnatural blinking. Early deepfake models rarely blinked, because blinking was underrepresented in training data. Modern models do blink, but the frequency, duration, and timing of blinks may still be abnormal — either too infrequent, too fast, or synchronized oddly with speech pauses.
03
Lighting inconsistency between face and background. If the light source for the face appears to come from a different direction than the light source in the rest of the scene, the face has been composited. Pay attention to where highlights fall on the nose and forehead versus how light falls on nearby objects.
04
Accessories across frames. Earrings, glasses, and chains are rendered fresh on each frame by most deepfake systems. Watch for jewelry that appears to slightly shift position, change shape, or briefly disappear between frames — particularly during motion or fast cuts.
05
Lip sync inconsistency. Watch whether mouth movements precisely match the audio phonemes. Minor delays (even 50–100ms), or mouth shapes that don't quite match the sounds being produced, are common. This is particularly visible on words with distinctive mouth shapes — "P" and "B" sounds require lips to touch and separate.
06
Emotional misalignment. The emotional expression on the AI-generated face may not quite sync with the emotional content of the words or the vocal tone. A genuine speaker's face shows micro-expressions that naturally accompany what they're saying — AI-composited faces can feel slightly "flat" or emotionally incoherent on close inspection.
07
Temporal flickering on the face. Pause a video at several random points and compare. The face region in a deepfake may show subtle frame-to-frame variations in skin tone, texture, or sharpness that the rest of the video does not — particularly in lower-quality productions. This is sometimes called "temporal inconsistency."
08
Resolution mismatch. The synthetic face may render at a slightly different effective resolution than the background or the rest of the body. This is more common in lower-effort deepfakes but is worth checking by zooming in — the face may appear cleaner or softer than the surroundings in a way that doesn't make physical sense for the shooting conditions.
🎙️
Voice Clones — What to Listen For
Applies to: phone calls, voicemails, audio messages, video call audio
Voice cloning technology has improved dramatically. As of 2023, tools available to anyone can clone a recognizable voice from as little as three seconds of audio, according to McAfee's Artificial Imposters study. But every synthesis system leaves acoustic signatures — patterns in how it handles the things that make real speech sound human.
01
Missing disfluencies. Real human speech contains natural "disfluencies" — brief hesitations ("um," "uh"), false starts, self-corrections, and subtle pauses mid-sentence. AI-synthesized speech tends to flow too cleanly. Every word is pronounced correctly, every sentence completes without error. This perfection is unnatural.
02
Unnatural cadence or pacing. Listen for speech rhythm that doesn't sound quite right — emphasis that lands in slightly odd places, pauses between words or sentences that don't match how the person naturally speaks, or a slightly mechanical syllabic rhythm even within otherwise fluent speech.
03
Breathing patterns. Real speakers breathe audibly — brief inhales before sentences, slight exhales mid-phrase. AI voice synthesis commonly omits breathing sounds entirely, or inserts them in unnatural positions. Listen for where the speaker "takes breath" — or whether they seem not to breathe at all.
04
Emotional flatness or inconsistency. Even AI systems trained to inject emotion often produce emotional tones that don't quite match the content of the words. A voice that sounds slightly too neutral for a distressed message, or emotional peaks that don't land on the words they logically should, are flags.
05
"Hollow" or digital vocal quality. A subtle metallic, hollow, or slightly reverb-like quality in the voice — particularly on sustained vowels — can indicate synthesis. It may be most noticeable when the voice is speaking loudly or with emphasis, as amplitude variations expose synthesis artifacts more readily.
06
Speech pattern inconsistencies. If you know the supposed speaker, consider: do they actually use those words? That phrasing? That accent? Voice cloning replicates vocal timbre but is constrained by the text it's given to speak — the clone may "sound like" someone but say things that person would never say.
07
Audio environment mismatch. Real calls and voice messages carry room acoustics — slight reverb from walls, background noise that changes when the speaker moves. If the voice sounds like it was recorded in an acoustic vacuum while the caller claims to be calling from a busy airport, that's worth noting.
Behavioral Red Flags — What the Attacker Is Asking You to Do
Technical tells become less reliable as generation quality improves. Behavioral patterns are far more durable — because the goal of a deepfake attack is to get you to do something, and that goal creates predictable behaviors regardless of how convincing the media is.
"Hi, it's Dad. I'm in an emergency situation — I can't talk long. I've been in an accident, my phone is damaged, I'm using someone else's phone. I need you to send $800 via Zelle right now, I'll explain everything later. Please don't call the family, I don't want to worry everyone. Can you do it now?"
This script — or close variants — is used in what the FTC calls "family emergency scams," increasingly paired with AI voice cloning of the supposed family member's voice.
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Urgency + money request in the same communication. Legitimate emergencies involving family do not require you to wire money to an unfamiliar account within minutes. The pressure to act before you can think is by design.
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Request to not verify through other channels. "Don't call your brother, it'll worry him" or "I can't have you calling the hospital right now" are manufactured reasons to prevent you from calling a known number and confirming the situation independently.
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Refusal to appear on video or switch platforms. A deepfake voice caller cannot show their face live and unscripted. Requests to keep communication on voice only, especially with technical excuses ("my camera is broken"), are a warning sign.
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Inability to answer personal verification questions. Ask something only the real person would know — a family memory, a pet's name, a shared private experience. Voice clone systems cannot generate correct answers to questions the attacker doesn't know.
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Payment method specificity. Requests for wire transfer, gift cards, cryptocurrency, or Zelle are chosen because they are difficult to reverse. Legitimate urgent situations don't require you to buy Amazon gift cards.
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Contact through an unexpected or unfamiliar channel. Receiving a distress call from an unfamiliar number, a LinkedIn message from an unusual email address, or a WhatsApp message claiming to be from a known contact is a structural red flag — attackers can't use the real person's established channels.
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Extreme story inconsistencies. AI attackers research targets but imperfectly. The "CFO" in a business email compromise may not know a project name. The "family member" may not remember what city they supposedly live in. Probe with specific details and watch for vagueness.
No single tool is definitive, but several are fast, free, and catch a significant proportion of what circulates. Use them as a first check — not as a final verdict.
Google Reverse Image SearchFree
Image verification
Drag any image to images.google.com to find where it appears online. If a "journalist" profile photo returns results from a completely unrelated context, it's likely stolen or synthetic. The fastest first check for any suspicious image.
Indexes images differently than Google — often finds older instances or sources that Google misses. Especially useful for tracking the original source of a manipulated image and identifying where it first appeared.
Performs error-level analysis (ELA) on images — areas that have been digitally altered appear at different compression levels than the original. Academic in nature but freely accessible. Highly useful for detecting spliced or manipulated images. Developed by Neal Krawetz.
Upload any image and receive a probability score estimating whether it was AI-generated. Covers Midjourney, DALL-E, Stable Diffusion, and other generators. Free tier allows a limited number of checks; paid tier removes limits.
A browser extension used by professional journalists at Reuters, the BBC, and AFP to verify video content. Extracts keyframes from video for reverse image search, checks metadata, and connects to multiple verification services. Developed by the InVID/WeVerify consortium with EU research funding — built for real-world use.
Submit a video URL or upload a clip to receive a deepfake probability score. Scans frame-by-frame and highlights segments flagged as likely synthetic. One of the more accessible free options for analyzing video deepfakes directly.
Unlike most AI detectors, Illuminarty also attempts to highlight which specific regions of an image are likely AI-generated — useful for detecting partially manipulated images where only a face or background was replaced.
Enterprise-grade platform covering video, audio, and image detection simultaneously. Used by media organizations, financial institutions, and governments. Not consumer-facing, but relevant context — it represents the state of the art in commercial detection capability as of 2026.
For academic research context on detection technology, the DARPA Media Forensics (MediFor) program has funded significant work in this area, and Hany Farid at UC Berkeley is among the most cited researchers in deepfake detection. Both are worth following if you want to track the technical frontier of the field.
What to Do When You're Not Sure
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Slow down deliberately. The most effective defense against deepfake fraud is time. Urgency is manufactured. If anyone — a caller claiming to be a family member, a CEO in a video message, a government official in a social post — is creating pressure to act immediately, that pressure is the attack. Take the time to verify.
✓
Call back on a known number. Never call back on the number that contacted you. If a family member calls in distress, hang up and call their actual phone number from your contacts. If a company calls you, hang up and call the main number from the company's official website. This simple step defeats most voice clone attacks.
✓
Establish a family code word. Choose a word or phrase that only your family knows, and agree to use it to verify identity in any suspicious emergency communication. A voice clone cannot produce a code word the attacker doesn't know. This is specifically recommended by the FTC and the AARP Fraud Watch Network.
✓
Run the four-second video call test. In any video call where identity matters, ask the person to do something unrehearsed — turn their head slowly to the side, hold up a specific object, or perform a simple physical action. Current real-time deepfake systems degrade visibly during unexpected movements and object interaction.
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Use a verification tool before sharing or acting. Before forwarding any image or video that seems significant or shocking, run it through Google Reverse Image Search and one AI detection tool. This takes 60 seconds and catches a substantial proportion of synthetic media in active circulation.
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Look for C2PA watermarks on trustworthy content. The Coalition for Content Provenance and Authenticity (C2PA) is developing a technical standard for cryptographic content credentials that certify the origin and editing history of media. Major camera manufacturers, Adobe, and some news organizations are implementing this. An image or video with verified C2PA credentials is significantly harder to fake.
✓
Ask the right questions about the content itself. Who benefits from you believing this? What would you need to do if it were true? Why is this reaching you this way, at this time? The answers to these questions reveal a lot about whether content is legitimate or manufactured.
If You've Been Targeted
If you've sent money, shared personal information, or been manipulated by content you now believe was AI-generated, act quickly — not to recover the money (which is often unrecoverable) but to document and report, which helps track and potentially stop the operation targeting others.
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Report to the FTC at reportfraud.ftc.gov. Fraud reports feed into the Consumer Sentinel Network used by law enforcement.
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File an IC3 complaint at ic3.gov — the FBI's Internet Crime Complaint Center. Especially important for voice clone and video-call fraud above $1,000.
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Call your bank immediately if a wire transfer was involved. Some international transfers can be recalled within 72 hours if reported fast enough — call, do not email.
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Contact the AARP Fraud Helpline at 877-908-3360 — available to all ages, not only AARP members. Staff are trained specifically on AI fraud schemes and can advise on next steps.
Conclusion
The tells are real. The defense is learnable.
Deepfake quality will continue to improve, and some of the specific visual artifacts described here will eventually be resolved by better generation systems. But the behavioral patterns of deepfake attacks — urgency, financial pressure, manufactured reasons to avoid verification — are structural features of the fraud, not features of the technology. Those won't change.
The most durable protection isn't a detection tool. It's the habit of slowing down before acting on anything that arrived unexpectedly, claimed to be urgent, and asked you to do something you'd normally verify. A phone call, a code word, an extra 90 seconds — these are not inconveniences. They are the difference between a close call and a $25 million wire transfer.
Sources and References
[1]
Reuters — "Hong Kong company loses HK$200 million in deepfake video conference scam," Feb 4, 2024. reuters.com ↗
[2]
FBI Internet Crime Complaint Center (IC3) — "Public Service Announcement: Malicious Actors Manipulating Photos and Videos to Create Explicit Content and Sextortion Schemes," June 28, 2022. ic3.gov ↗
[3]
McAfee — "Artificial Imposters: Cybercriminals Turn to AI Voice Cloning for a New Breed of Scam," 2023. mcafee.com ↗
[4]
Sumsub — "The Sumsub Identity Fraud Report," 2023 — documents 10× increase in detected deepfakes year-over-year. sumsub.com ↗
[5]
Europol — "Malicious Uses and Abuses of Artificial Intelligence" — co-authored with UNICRI and Trend Micro; covers deepfake threat landscape. europol.europa.eu ↗
[6]
European Parliament — "Deepfakes and Audiovisual Disinformation" briefing, 2021. EPRS_BRI(2021)690039. europarl.europa.eu ↗
[7]
Stanford Internet Observatory — research on synthetic media, disinformation, and digital forensics. cyber.fsi.stanford.edu ↗
[8]
DARPA — Media Forensics (MediFor) program; automated detection of manipulated media from 2016 onward. darpa.mil ↗
[9]
Hany Farid, UC Berkeley — prolific deepfake detection researcher; research page covers detection methodology and published findings. farid.berkeley.edu ↗
[10]
Sensity AI (formerly Deeptrace) — tracks and categorizes deepfake content globally; publishes annual state-of-deepfakes reports. sensity.ai ↗
[11]
InVID WeVerify Project — EU-funded video verification toolkit used by professional journalists and fact-checkers. invid-project.eu ↗
[12]
FTC — "What To Know About Family Emergency Scams" — recommends code words and callback verification protocols. consumer.ftc.gov ↗
[13]
WITNESS — media preservation and video verification resources for human rights and general public use. witness.org ↗
[14]
C2PA — Coalition for Content Provenance and Authenticity; open technical standard for verifiable content credentials. c2pa.org ↗
S
Sheldon Valentine
Founder · Dear Tech
Sheldon writes about AI safety, tools, and the practical knowledge people need to navigate an AI-saturated world. Dear Tech's editorial commitment is honest, specific, and never alarmist — but also never dismissive of risks that are real and present.
