ADVERFACIAL: PRIVACY-PRESERVING UNIVERSAL ADVERSARIAL PERTURBATION AGAINST FACIAL MICRO-EXPRESSION LEAKAGES

Privacy safeguards are crucial, notably 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 conceal 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. This paper proposes the novel universal adversarial perturbation-based approach - AdverFacial - to privacy protection 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 micro-expression spotting and recognition models and demonstrate our approach's effectiveness in emotional privacy protection.