Although Deep Neural Networks (DNNs) achieve excellent performance on many real-world tasks, they are highly vulnerable to adversarial attacks. A leading defense against such attacks is adversarial training, a technique in which a DNN is trained to be robust to adversarial attacks by introducing adversarial noise to its input. This procedure is effective but must be done during the training phase. In this work, we propose Augmented Random Forest (ARF), a simple and easy-to-use strategy for robustifying an existing pretrained DNN without modifying its weights. For every image, we generate randomized test time augmentations by applying diverse color, blur, noise, and geometric transforms. Then we use the DNN's logits output to train a simple random forest to predict the real class label. Our method achieves state-of-the-art adversarial robustness on a diversity of white and black box attacks with minimal compromise on the natural images' classification. We test ARF also against numerous adaptive white-box attacks and it shows excellent results when combined with adversarial training. Code is available at https://github.com/giladcohen/ARF.
Enhancing Face Recognition with Latent Space Data Augmentation and Facial Posture Reconstruction
School of Computer Engineering, Iran University of Science and Technology（伊朗科技大学计算机工程学院）；Technical and Engineering Faculty, South Tehran Branch, Islamic Azad University（伊斯兰阿扎德大学南德黑兰分校技术与工程学院）；Faculty of Engineering, University of Birjand（比尔詹德大学工程学院）；Faculty of Computer Engineering & Information Technology, Shahrood University of Technology（沙赫鲁德工业大学计算机工程与信息技术学院）
University of Toronto（多伦多大学）；LG AI Research（LG人工智能研究院）
Recent years have seen the introduction of a range of methods for post-hoc explainability of image classifier predictions. However, these post-hoc explanations may not always be faithful to classifier predictions, which poses a significant challenge when attempting to debug models based on such explanations. To this end, we seek a methodology that can improve the faithfulness of an explanation method with respect to model predictions which does not require ground truth explanations. We achieve this through a novel explanation-driven data augmentation (EDDA) technique that augments the training…
State University of New York College of Environmental Science and Forestry（纽约州立大学环境科学与林业学院）
Novel plant communities reshape landscapes and pose challenges for land cover classification and mapping that can constrain research and stewardship efforts. In the US Northeast, emergence of low-statured woody vegetation, or shrublands, instead of secondary forests in post-agricultural landscapes is well-documented by field studies, but poorly understood from a landscape perspective, which limits the ability to systematically study and manage these lands. To address gaps in classification/mapping of low-statured cover types where they have been historically rare, we developed models to predic…
