DigitalMANIPULATIVE

Cheapfakes (Low-Tech Media Manipulation)

What it is

Deceptive audiovisual material made with ordinary tools (slowing, speeding, cropping, splicing, or simply recaptioning real footage) rather than with machine learning. Attention goes to deepfakes; most of the damage is done by these.

How it works

Britt Paris and Joan Donovan introduced the term in 2019 to correct a skewed threat picture. They placed audiovisual manipulation on a spectrum from technically demanding deepfakes to cheap fakes that need only a phone or free editing software, and argued that the cheap end is more common and harder to regulate because it uses real footage. The mechanism is that viewers treat video as a direct record. A real clip with its speed altered, its context removed, or a false caption attached keeps all the authenticity cues of the original: real faces, real voices, real settings. No detector flags it as synthetic, because nothing is. Meta reported that during the major elections of 2024, AI-generated content made up less than one percent of the election-related misinformation its fact-checkers rated. A public trained to look for AI artifacts is looking in the wrong place most of the time.

Real-world examples

  • •In May 2019 a video of U.S. House Speaker Nancy Pelosi slowed to about 75 percent speed, making her speech sound slurred, drew millions of views on Facebook, which declined to remove it and instead reduced its distribution and attached fact-check labels.
  • •In November 2018 the White House press secretary shared a clip of CNN reporter Jim Acosta's arm contacting an intern as she reached for his microphone; independent video analysts concluded that frames had been altered in a way that made the motion look more forceful.
  • •In February 2020 Michael Bloomberg's presidential campaign posted a debate clip edited to insert a long silence with cricket sounds after he asked his Democratic rivals a question, which did not happen; Twitter said the clip would have been labeled under its then-forthcoming manipulated media policy.
  • •In conflicts including Ukraine from 2022 and Gaza from 2023, fact-checkers at Reuters, AFP, and the BBC repeatedly traced viral battlefield videos to video-game footage or to earlier, unrelated wars.

Ethical guidelines

  • ●Editing for length is normal; editing that changes what a reasonable viewer would believe happened is fabrication, whatever software was used.
  • ●Satire should be recognizable as satire to the audience it will actually reach, not just to the people who made it.
  • ●A true clip with a false caption is a false claim. Sharing it is sharing the caption.

How to defend against it

  • ►Ask the context questions before the authenticity question: when was this filmed, where, and what happened in the thirty seconds before and after? Most cheapfakes fail on date and place, not on pixels.
  • ►Find the longer original. Search a distinctive phrase from the clip plus the event name; C-SPAN, official feeds, and wire agencies usually host the uncut version.
  • ►Run a reverse image search on a key frame (Google Lens, TinEye, Yandex). Recycled footage from an older event often surfaces in the first page of results.
  • ►Distrust clips that start or end mid-sentence, have no ambient sound, or carry only a caption telling you what you are seeing.
  • ►Use the SIFT sequence (Caulfield): stop, investigate the source, find better coverage, trace the clip to its origin, before sharing.

References

  1. Paris, B., & Donovan, J. (2019). Deepfakes and Cheap Fakes: The Manipulation of Audio and Visual Evidence. Data & Society Research Institute · link
    The cheap fake concept, the spectrum of audiovisual manipulation, and the Pelosi and Acosta cases.
  2. Clegg, N. (Meta) (2024). What We Saw on Our Platforms During 2024's Global Elections. Meta Newsroom, December 3, 2024
    Ratings on AI content were less than 1 percent of fact-checked election-related misinformation on Meta platforms in 2024; a platform self-report.
  3. Caulfield, M., & Wineburg, S. (2023). Verified: How to Think Straight, Get Duped Less, and Make Better Decisions about What to Believe Online. University of Chicago Press
    The SIFT method and tracing media to its original context as a defense.
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