Lie to Me: Shield Your Emotions from Prying Software

Abstract

Deep learning approaches for facial Emotion Recognition (ER) obtain high accuracy on basic models, e.g., Ekman’s models, in the specific domain of facial emotional expressions. Thus, facial tracking of users’ emotions could be easily used against the right to privacy or for manipulative purposes. As recent studies have shown that deep learning models are susceptible to adversarial examples (images intentionally modified to fool a machine learning classifier) we propose to use them to preserve users’ privacy against ER. In this paper, we present a technique for generating Emotion Adversarial Attacks (EAAs). EAAs are performed applying well-known image filters inspired from Instagram, and a multi-objective evolutionary algorithm is used to determine the per-image best filters attacking combination. Experimental results on the well-known AffectNet dataset of facial expressions show that our approach successfully attacks emotion classifiers to protect user privacy. On the other hand, the quality of the images from the human perception point of view is maintained. Several experiments with different sequences of filters are run and show that the Attack Success Rate is very high, above 90% for every test.

Are You Concerned about Limited Function Evaluations: Data-Augmented Pareto Set Learning for Expensive Multi-Objective Optimization

“…Expensive multi-objective optimization problems (EMOPs) are commonly seen in various real-world applications (Jablonka et al 2021;Baia et al 2022;Xie et al 2021;Yang et al 2023). These problems typically entail conflicting objectives and costly evaluations, such as antenna structure design (Ding et al 2019), clinical drug trials (Yu, Ramakrishnan, and Meinzer 2019), and neural network structure search (Lu et al 2019), etc.…”