Skip to Main Content
It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.
click map TCC Home TCC Library Home

Media Literacy: Deceptive Imagery

Media literacy encompasses the practices that allow people to access, critically evaluate, and create or manipulate media.

Take this short quiz to learn more about deceptive imagery

Can you spot the AI-created photo? Test your deceptive imagery detection skills with this game

This game was developed by Jevin West and Carl Bergstrom, authors of the book Calling Bullsh*t. Their course website, CallingBull.org, provides video lectures, tools, and case studies to educate the public on data literacy.

Learn More about Deceptive Imagery and How to Identify It


The Washington Post published "The Fact Checker’s Guide to Manipulated Video." The news organization "set out to develop a universal language to label manipulated video and hold creators and sharers of this misinformation accountable." 

Missing Context

  • Misrepresentation: unaltered video footage that is misrepresented
  • Isolation: sharing only part of a clip that removes the original context

Deceptive Editing

  • Omission: cutting out portions of a video to remove important elements
  • Splicing: editing multiple videos together to create a false narrative

Malicious Transformation

  • Doctoring: editing the frames of a video to alter the visuals and audio
  • Fabrication: (deepfakes) using AI to alter the audio and visuals

Visit the "The Fact Checker’s Guide to Manipulated Video" to see specific examples or to submit a video you think have been manipulated for The Washington Post to review.

Authors and educators Jevin West and Carl Bergstrom go into more depth about data visualizations, specifically misleading axes and the principle of proportional ink on their website, CallingBull.org. And educator Lea Gaslowitz has an extended lesson on misleading data visualizations. If you'd like to learn more, visit the TED-Ed site.


Trace Claims and Quotes to the original source

Resources you can use if you find a video or image you suspect is fake
  • Sensity.AI - Sign up for an account to use its deepfake detector.
  • TinEye - Reverse image search to track the image online and locate the original.
  • Google Images - Reverse Image Search
  • Fauxtography - Snopes site for fact-checking viral videos and images

Investigate Sources     

Norton Antivirus published a list of techniques you can use to spot a deepfake. Their tips include:
  • "Unnatural eye movement. Eye movements that do not look natural — or a lack of eye movement, such as an absence of blinking — are red flags. It’s challenging to replicate the act of blinking in a way that looks natural. It’s also challenging to replicate a real person’s eye moments. That’s becomes someone’s eyes usually follow the person they’re talking to.
  • Unnatural facial expressions. When something doesn’t look right about a face, it could signal facial morphing. This occurs when a simple stich of one image has been done over another.
  • Awkward facial-feature positioning. If someone’s face is pointing one way and their nose is pointing another, you should be skeptical about the video’s authenticity.
  • A lack of emotion. You also can spot facial morphing or image stiches if someone’s face doesn’t seem to exhibit the emotion that should go along with what they’re supposedly saying.
  • Awkward-looking body or posture. Another sign is if a person’s body shape doesn’t look natural or there is awkward or inconsistent positioning of head and body. This may be one of the easier inconsistencies to spot, because deepfake technology usually focuses on facial features rather than the whole body.
  • Unnatural body movement. If someone looks distorted or off when they turn to the side or move their head, or their movements are jerky and disjointed from one frame to the next, you should suspect the video is fake.
  • Unnatural coloring. Abnormal skin tone, discoloration, weird lighting, and misplaced shadows are all signs that what you’re seeing is likely fake.
  • Hair that doesn’t look real. You won’t see frizzy or flyaway hair, because fake images won’t be able to generate these individual characteristics.
  • Teeth that don’t look real. Algorithms may not be able to generate individual teeth, so an absence of outlines of individual teeth could be a clue.
  • Blurring or misalignment. If the edges of images are blurry or visuals are misaligned —  for example, where someone’s face and neck meet their body — you’ll know something is amiss.
  • Inconsistent noise or audio. Deepfake creators usually spend more time on the video images rather than the audio. The result can be poor lip-syncing, robotic-sounding voices, strange word pronunciation, digital background noise, or even the absence of audio."

Metro Campus Library: 918.595.7172 | Northeast Campus Library: 918.595.7501 | Southeast Campus Library: 918.595.7701 | West Campus Library: 918.595.8010
email: Library Website Technical Help | TCC Acceptable Use Policy | MyTCC | © 2021 Tulsa Community College