Adversarial Example Detection with Latent Representation Dynamic Prototype

In the realm of Deep Neural Networks (DNNs), one of the primary concerns is their vulnerability in adversarial environments, whereby malicious attackers can easily manipulate them. As such, identifying adversarial samples is crucial to safeguarding the security of DNNs in real-world scenarios. In this work, we propose a method of adversarial example detection. Our approach using a Latent Representation Dynamic Prototype to sample more generalizable latent representations from a learnable Gaussian distribution, which relaxes the detection dependency on the nearest neighbour's latent representation. Additionally, we introduce Random Homogeneous Sampling (RHS) to replace KNN sampling reference samples, resulting in lower reasoning time complexity at O(1). Lastly, we use cross-attention in the adversarial discriminator to capture the evolutionary differences of latent representation in benign and adversarial samples by comparing the latent representations from inference and reference samples globally. We conducted experiments to evaluate our approach and found that it performs competitively in the gray-box setting against various attacks with two Lp-norm constraints for CIFAR-10 and SVHN datasets. Moreover, our detector trained with PGD attack exhibited detection ability for unseen adversarial samples generated by other adversarial attacks with small perturbations, ensuring its generalization ability in different scenarios.