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Videos called ‘deep fakes’ are created by an algorithm, based on deep learning techniques, that matches one individual’s (the target) facial patterns to another’s (the source). These videos are compelling. In many cases they are visually indistinguishable from real recordings. Being able to identify fake videos is critical to refuting the misinformation that they may be used to promulgate. While some approaches have been proposed, they are largely based on exploiting temporary gaps in the video production algorithm (such as not introducing significant blinking) that may be fixed in the future.
This paper proposes a prospective systematic way of evaluating the veracity of possible deep fake videos. The proposed approach is based on assessing the color patterns and variations of the target’s face. Videos produced by the construction algorithm are compared to a similar actual recording. Also, videos made using the construction algorithm based on using multiple sources are compared to each other and to the actual recording of the target and key differences between the different videos are discussed. The paper then proceeds to analyze whether the identified differences and patterns are suitable for differentiating fake videos from actual recordings, across multiple application areas. The paper concludes with a discussion of the impact of so-called ‘deep fakes’ technology on public trust in video recordings and the media. The impact of a technique, like the proposed, on multiple application areas is discussed. Its efficacy for multiple critical applications is considered and topics for next steps are discussed.
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