Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks

Understand

Deep neural network (DNN) architecture based models have high expressive power and learning capacity.

• However, they are essentially a black box method since it is not easy to mathematically formulate the functions that are learned within its many layers of representation.
• Realizing this, many researchers have started to design methods to exploit the drawbacks of deep learning based algorithms questioning their robustness and exposing their singularities.
• In this paper, we attempt to unravel three aspects related to the robustness of DNNs for face recognition: (i) assessing the impact of deep architectures for face recognition in terms of vulnerabilities to attacks inspired by commonly observed distortions in the real world that are well handled by shallow learning methods along with learning based adversaries; (ii) detecting the singularities by characterizing abnormal filter response behavior in the hidden layers of deep networks; and (iii) making corrections to the processing pipeline to alleviate the problem.