Robust Recovery of Adversarial Samples

Adversarial examples are semantically associated with one class, but modern Deep Learning architectures fail to see the semantics and associate them to another idea. As a result, these examples pose a profound risk to almost every Deep Learning architecture. Our proposed architecture is composed of a U-Net with the characteristics of Self Attention & Cross Attention. It can recover such examples effectively with more than 4x the magnitude of attacks that the state-of-the-art is capable of despite having lesser parameters than the VGG-13 model. Our study also encompasses the differences in the results between Noise and Image reconstruction of such examples.