AdverFacial: Privacy-Preserving Universal Adversarial Perturbation Against Facial Micro-Expression Leakages

Abstract

Privacy safeguards are crucial, notably now with increased virtual conferencing usage during the Covid pandemic. In contrast to conventional facial expressions that are visually obvious to humans, micro-expressions are involuntary and transient facial expressions, commonly manifested involuntarily when we aim to withhold our emotions. Advanced micro-expression recognition techniques exist that can reveal the genuine emotions that people attempt to conceal, thus threatening individual emotional privacy, as fundamental human rights would dictate that one should have a choice of what emotion is being shown or not shown. We propose the novel universal adversarial perturbation-based approach - AdverFacial - for privacy concealment against automated micro-expression analysis via deep learning techniques. We derive the optimal strategy to achieve micro-expression misclassification with a high success rate, low perceptibility and cross neural network transferability. We perform experiments on two popular datasets with state-of-the-art microexpression spotting and recognition models and demonstrate our approach’s effectiveness in emotional concealment.