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Challenges in detecting deepfakes, including novel generative models and compressed social media posts
A range of visual cues — inconsistent lighting and shadows, blurred edges and smudged facial regions, mismatched lip and eye behavior, unnatural skin texture or symmetry, and background or reflection ...
Deepfakes have been weaponized across healthcare to impersonate trusted clinicians, promote unproven products, manipulate medical images and carry out social‑engineering attacks that steal data or pre...
Forensic analysts detect deepfakes in medical ads by combining image- and audio-level signal analysis (pixel/texture noise, compression artifacts), biometric and behavioral checks (eye blinks, pupil d...
Deepfake detection teams combine signal-forensic analysis, machine‑learning classifiers, and domain‑specific checks — looking for software artifacts, physiological or physical impossibilities, audio‑v...
Deepfake detection tools combine machine learning, signal analysis and forensic metadata checks to flag manipulated audio and video, and a growing set of commercial and open services are available to ...