Deepfake Detection in the Era of Artificial Intelligence: A Practice of Crime Prevention Strategies

The purpose of this study is to demonstrate how to create deepfake contents and to identify an efficient and robust platform for detecting deepfakes. Also, this study conducts an empirical test on the application of the situational crime prevention techniques for preventing deepfake misuse. To that end, this research evaluates the current deepfake generation platforms and the newly updated deepfake detection techniques using a dataset of deepfake generated images/videos. The results of this study indicate that either increasing the effort required for criminals (e.g., setting platforms to block malicious content generation) or by increasing the risk of being caught through enhanced detection systems help deter criminals by building more difficult environment(s) to commit deepfake-related crimes. Policy implications and limitations are discussed.