Defense against adversarial attacks on deep learning in computer vision

计算机视觉应用严重依赖于深度学习，这是非常容易被愚弄添加微妙的扰动对象/图像纹理是人类无法察觉的。该项目旨在开发防御机制，以检测和删除输入图像中的对抗模式。该项目希望在理解深度学习的脆弱性方面取得进展，并设计内在强大的深度学习架构。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行业受益。

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.