VLCAP: Vision-Language With Contrastive Learning For Coherent Video Paragraph Captioning

Wasserstein metric based adversarial attacks have attracted a great interest in the recent past. Even though they exhibit strong attacks, surprisingly, they have not been investigated for defense. in this work, we demonstrate that barycenters computed in Wasserstein space can act as a measure of defense against adversarial attacks. We compute the barycenter using marginals obtained from the given image and demonstrate its effectiveness in defense even without any adversarial training. We further analyse the barycenters using GradCam to understand their defensive characteristics. Elaborate experiments on MNIST, Fashion-MNIST, CIFAR-10 and CIFAR-100 demonstrate a significant increase in the robustness of victim classifiers.