Improving Robustness of Facial Landmark Detection by Defending against Adversarial Attacks.

Many recent developments in facial landmark detection have been driven by stacking model parameters or augmenting annotations. However, three subsequent challenges remain, including 1) an increase in computational overhead, 2) the risk of overfitting caused by increasing model parameters, and 3) the burden of labor-intensive annotation by humans. We argue that exploring the weaknesses of the detector so as to remedy them is a promising method of robust facial landmark detection. To achieve this, we propose a sample-adaptive adversarial training (SAAT) approach to interactively optimize an attacker and a detector, which improves facial landmark detection as a defense against sample-adaptive black-box attacks. By leveraging adversarial attacks, the proposed SAAT exploits adversarial perturbations beyond the handcrafted transformations to improve the detector. Specifically, an attacker generates adversarial perturbations to reflect the weakness of the detector. Then, the detector must improve its robustness to adversarial perturbations to defend against adversarial attacks. Moreover, a sample-adaptive weight is designed to balance the risks and benefits of augmenting adversarial examples to train the detector. We also introduce a masked face alignment dataset, Masked-300W, to evaluate our method. Experiments show that our SAAT performed comparably to existing state-of-the-art methods. The dataset and model are publicly available at https://github.com/zhuccly/SAAT.