Adversarial Training for Free!

Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks. Unfortunately, the high cost of generating strong adversarial examples makes standard adversarial training impractical on large-scale problems like ImageNet. We present an algorithm that eliminates the overhead cost of generating adversarial examples by recycling the gradient information computed when updating model parameters. Our "free" adversarial training algorithm achieves comparable robustness to PGD adversarial training on the CIFAR-10 and CIFAR-100 datasets at negligible additional cost compared to natural training, and can be 7 to 30 times faster than other strong adversarial training methods. Using a single workstation with 4 P100 GPUs and 2 days of runtime, we can train a robust model for the large-scale ImageNet classification task that maintains 40% accuracy against PGD attacks. The code is available at https://github.com/ashafahi/free_adv_train.Comment: Accepted to NeurIPS 201

Generate, Segment and Refine: Towards Generic Manipulation Segmentation

Detecting manipulated images has become a significant emerging challenge. The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet. While the intent behind such manipulations varies widely, concerns on the spread of fake news and misinformation is growing. Current state of the art methods for detecting these manipulated images suffers from the lack of training data due to the laborious labeling process. We address this problem in this paper, for which we introduce a manipulated image generation process that creates true positives using currently available datasets. Drawing from traditional work on image blending, we propose a novel generator for creating such examples. In addition, we also propose to further create examples that force the algorithm to focus on boundary artifacts during training. Strong experimental results validate our proposal

Medial collateral ligament injuries of the knee: current treatment concepts

The medial collateral ligament is one of the most commonly injured ligaments of the knee. Most injuries result from a valgus force on the knee. The increased participation in football, ice hockey, and skiing has all contributed to the increased frequency of MCL injuries. Prophylactic knee bracing in contact sports may prevent injury; however, performance may suffer. The majority of patients who sustain an MCL injury will achieve their pre-injury activity level with non-operative treatment alone; however, those with combined ligamentous injuries may require acute operative care. Accurate characterization of each aspect of the injury will help to determine the optimum treatment plan