Cheapfakes vs Deepfakes

“Cheapfake” and “deepfake” are informal labels rather than precise forensic diagnoses. Cheapfakes generally describe relatively simple manipulation or misleading context: a clip slowed down, a crop that removes surrounding action, reordered segments, or an authentic photo paired with a false caption. Deepfake is often used for media generated or altered with machine-learning techniques, such as a face or voice transformation. The boundary can blur because people may combine ordinary editing with generated media. The distinction concerns a possible production method, not a truth test. A genuine video can mislead when its date or context is changed. A deepfake can be shared with an accurate explanation of a fictional scene, while an unaltered recording may still omit relevant context. “Looks strange” is not enough to call something a deepfake; compression, lighting, subtitles, edits, and playback can affect appearance. A familiar face or realistic voice does not authenticate the claim either. Investigate the specific assertion. Find the complete clip or image, identify the earliest available post, search distinctive frames, compare edits, and look for original-source context. Verify date and location using independent reporting, public records, or knowledgeable sources. For consequential claims, seek corroboration from sources with direct access. If technical analysis is needed, preserve the original file and document the method; a social-media download may already have been transcoded. C2PA Content Credentials can carry signed provenance information about an asset’s origin or editing history when present and preserved. Their presence does not make a caption true, and their absence does not prove manipulation: credentials may never have been attached or may be lost through ordinary workflows. Describe what can be observed and what remains unknown. Both simple edits and advanced synthesis call for verification of provenance, context, and the claim itself.