How AI Goes Wrong:
From Hallucinations and Bias to Synthetic Abuse
AI risk is not limited to fake images or cloned voices. More often, AI invents details confidently, amplifies bias, or wraps authentic material in false context. This chapter maps those risks together so you are not only defending against deepfakes, but understanding why AI-generated or AI-amplified content misleads people at all.
The Complete Manipulation Technology Spectrum
| Type | Tech Threshold | Cost | Detection Difficulty | Primary Use |
|---|---|---|---|---|
| Cheapfake | ⭐ | $0 | ⭐⭐ | Political attack, emotional manipulation |
| Photoshop | ⭐⭐ | Low | ⭐⭐⭐ | Faking crime scenes, forging documents |
| GAN Synthetic Faces | ⭐⭐⭐ | Medium | ⭐⭐⭐⭐ | Fake accounts, fake review farms |
| Face Swap | ⭐⭐⭐⭐ | Medium | ⭐⭐⭐⭐ | Political disinformation, non-consensual sexual content |
| Voice Cloning | ⭐⭐⭐ | Low | ⭐⭐⭐⭐⭐ | Fraud, political interference |
| Multimodal Deepfake | ⭐⭐⭐⭐⭐ | High | ⭐⭐⭐⭐⭐ | Corporate fraud, high-value deception |
Three Types of Cheapfakes
The term "cheapfake" was popularized by journalist Nina Schick and Sam Gregory (WITNESS media watchdog) to describe manipulated media created using simple, low-cost techniques without AI.
- Speed manipulation: Adjust playback speed (usually slowed to 70-80%). Effect: Anyone appears drunk or mentally sluggish. Detection: Notice voice pitch (slower speed = lower pitch) and background sounds (ambient audio abnormally low-pitched).
- Context stripping: Keep only out-of-context clips so politicians' or experts' words seem completely different. Detection: Search for original full video and check surrounding context.
- Loop editing: Cut a few-second clip into a seamless loop to make viewers believe an event lasted much longer (common in crowd violence, explosions, protest scenes). Detection: Carefully watch for repeating objects in the background (cloud movement, crowd positioning).
GAN Synthetic Faces: Identifying "People Who Don't Exist"
Technologies like StyleGAN and Stable Diffusion can generate highly realistic photos of "people who don't exist," widely used to create fake social media accounts, fake review farms, and forged expert credentials.
- Asymmetric ears: GAN faces often have oddly shaped ears, or clearly asymmetric left-right ears
- Abnormal background: Background objects may merge, straight lines curve, objects "disappear"
- Inconsistent eye catchlights: Real eyes have nearly identical catchlights in both eyes; GAN faces often have different catchlights in each eye
- Hair and teeth anomalies: Fine strands of hair may merge into blobs; teeth may have wrong count or abnormally perfect edges
- Necklaces and glasses: These two items are where GANs most often fail — may be asymmetric or bizarrely shaped
How to Detect Face Swap Deepfakes
Face swap deepfakes use deep learning to "paste" one person's facial features onto another person's body video. Common technologies include DeepFaceLab, FaceSwap, and various NVIDIA face-swapping models.
- Facial boundary halos: Face swap edges often show semi-transparent "halos" during lighting changes, especially in profile views, low light, or fast movement
- Abnormal blink rate: Early deepfake tech rarely blinked (fewer closed-eye training images); modern deepfakes may blink excessively
- Head rotation artifacts: When the head rapidly turns beyond 45 degrees to the side, facial rendering quality visibly degrades
- Skin tone boundaries: Under different lighting, the swapped face's skin tone may not match the neck or ears
- Lip sync mismatch: Especially in specific languages (like Chinese), lip movement may not perfectly match the audio
AI Voice Cloning: When the Phone Isn't Who You Think
Modern AI voice cloning technology (like ElevenLabs, Coqui TTS, OpenAI's Voice Engine) requires only 3-5 seconds of voice sample to generate convincing clones. Cost: virtually zero. This dramatically lowers the technical barrier for phone fraud.
- Abnormal breathing rhythm: AI voices often lack natural breath sounds, or breathing occurs at unnatural sentence positions
- Overly flat prosody: Emotional passages (anger, excitement, sadness) have less natural pitch variation than real humans — sounds like "reading a script"
- Background audio "splicing feel": There may be slight volume or audio quality switching between AI-synthesized voice portions and background ambient sound
- Specific pronunciation errors: Chinese dialects, Taiwanese, regional accents, and technical terms are where AI voice cloning most often fails
Multimodal Deepfakes: The Most Dangerous Combination Attack
Single-modality deepfakes (only visual, or only voice) are relatively easier to detect. But when attackers simultaneously fake visual, audio, and text modalities, the three mutually "confirm" each other, dramatically improving deception success rates. This is called "multimodal deepfake attack" — currently the most technically mature and dangerous form of deepfake.
Typical attack flow: Attackers first collect public videos and audio of the target (e.g., a corporate executive); use face swap to generate deepfake video; use voice cloning to generate audio; and forge email to "confirm" the instructions. The victim sees the video, hears the voice, receives the email — three channels all "pointing to the same instruction" — therefore believing its authenticity.
Slide Deck
Case Studies
About three weeks after Russia's invasion of Ukraine, a deepfake video began circulating on Telegram, Facebook, and Twitter in which "Ukrainian President Zelensky" ordered soldiers to lay down their weapons. The video first appeared in a hacked Ukrainian TV station's live broadcast, then spread massively on social media.
Technical analysis showed this was a lower-quality face swap deepfake: head disproportionately large relative to shoulders (typical insufficient training data problem), clear semi-transparent halo at face-neck boundary — especially obvious when the head turns. The voice pitch was about half a semitone higher than the real Zelensky, lacking his distinctive speech cadence.
Both Meta and YouTube flagged and removed the video within hours. The real Zelensky immediately released a rebuttal video filmed outside government buildings, emphasizing "We are here, I have not surrendered." Ukrainian fact-checking organization StopFake published a verification report within 90 minutes of the video appearing.
This case illustrates an important principle: Even technically low-quality deepfakes can be effective under certain social conditions (war panic). Defense strategy: Any video involving major political decisions or high-consequence statements like "surrender/attack" must wait for official media and government channel confirmation, not be judged based on the first social media source.
From November 2023, Taiwan saw a flood of deepfake video ads featuring celebrities (including TSMC founder Morris Chang, Terry Gou, Lai Ching-te, and others) massively distributed through YouTube and Facebook ad systems. These videos claimed celebrities "personally endorsed" investment platforms promising high returns, precisely targeting retirees.
Related fraud cases are estimated to have caused over NT$1 billion in losses for Taiwanese citizens, with multiple victims losing their life savings. Technical analysis showed these videos shared common tells: ① Lip movement not perfectly synced with Chinese dubbing ② Slight "melting" at hair edges when turning sideways ③ Direct eye contact with almost no blinking ④ Unnaturally applied background blur.
In a 2024 New Taipei District Court ruling, the court accepted AI forensic reports as supporting evidence confirming the defendant used deepfake technology to produce advertising videos, sentencing them to 4 years and 6 months imprisonment. This was one of the first deepfake fraud convictions in Taiwan using AI forensic reports as primary supporting evidence.
The ad system's "trust endorsement effect" makes these deepfakes especially dangerous: appearing in YouTube/Facebook ads makes people unconsciously think "the platform has already reviewed this ad." Most effective defense: For any celebrity "investment endorsement," find confirmation on the celebrity's official account or in mainstream media coverage. Any "endorsement" that never appeared in official channels is 100% a scam.