NEO: Navigating Entropy in Optimized Closed-Box Video Adversarial Attacks

Researchers developed NEO, a method for conducting adversarial attacks (adding subtle, imperceptible changes to videos to trick AI recognition systems) on deep learning video models more efficiently. NEO uses information entropy (a measure of uncertainty in data) to focus its attacks on the most informative points near decision boundaries (the threshold where a model switches from one prediction to another), achieving better attack success rates while requiring fewer queries to the target system.