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Adversarial Learning

1235 directly classified papers

Papers per year

Papers

Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples CVPR 2019
Exact Adversarial Attack to Image Captioning via Structured Output Learning With Latent Variables CVPR 2019
Adversarial Attacks Beyond the Image Space CVPR 2019
What Does It Mean to Learn in Deep Networks? And, How Does One Detect Adversarial Attacks? CVPR 2019
Handwriting Recognition in Low-Resource Scripts Using Adversarial Learning CVPR 2019
Adversarial Defense Through Network Profiling Based Path Extraction CVPR 2019
Detection Based Defense Against Adversarial Examples From the Steganalysis Point of View CVPR 2019
Knockoff Nets: Stealing Functionality of Black-Box Models CVPR 2019
ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples CVPR 2019
Curls & Whey: Boosting Black-Box Adversarial Attacks CVPR 2019
Barrage of Random Transforms for Adversarially Robust Defense CVPR 2019
Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition CVPR 2019
Robustness via Curvature Regularization, and Vice Versa CVPR 2019
SparseFool: A Few Pixels Make a Big Difference CVPR 2019
Generating 3D Adversarial Point Clouds CVPR 2019
Catastrophic Child's Play: Easy to Perform, Hard to Defend Adversarial Attacks CVPR 2019
AIRD: Adversarial Learning Framework for Image Repurposing Detection CVPR 2019
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations CVPR 2019
Trust Region Based Adversarial Attack on Neural Networks CVPR 2019
Learning Cross-Modal Embeddings With Adversarial Networks for Cooking Recipes and Food Images CVPR 2019
Robustness of 3D Deep Learning in an Adversarial Setting CVPR 2019
Adversarial Active Exploration for Inverse Dynamics Model Learning CORL 2019
Fisher-Bures Adversary Graph Convolutional Networks UAI 2019
Efficient Neural Network Verification with Exactness Characterization UAI 2019
GEM: Generative Enhanced Model for adversarial attacks EMNLP 2019