Defense against adversarial attacks on deep learning in computer vision

计算机视觉应用在很大程度上依赖于深度学习，而深度学习很容易被人类无法察觉的物体/图像纹理添加微妙的扰动所欺骗。该项目旨在开发防御机制，以从输入图像中检测和去除对抗模式。该项目希望在理解深度学习漏洞方面取得进展，并设计出固有鲁棒性的深度学习架构。该项目的成果将通过检测、报告和消除此类攻击来提高计算机视觉的安全性和可靠性，并将在许多方面使公众和工业界受益。

Computer vision applications rely heavily on deep learning, which is highly vulnerable to being fooled by adding subtle perturbations to object/image textures that are imperceptible to humans. This project aims to develop defense mechanisms to detect and remove adversarial patterns from the input images. The project expects to advance knowledge in understanding the vulnerabilities of deep learning, and to design deep learning architectures that are inherently robust. The outcomes of this project will increase the security and reliability of computer vision by detecting, reporting and nullifying such attacks and will benefit the general public and industry on many fronts.