{"list":"other","total":69,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"2105.05233","title":"Diffusion Models Beat GANs on Image Synthesis","created":"2021-05-11","cited_by_count":8255},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"2307.15043","title":"Universal and Transferable Adversarial Attacks on Aligned Language Models","created":"2023-07-27","cited_by_count":3104},{"id":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"id":"1903.12261","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","created":"2019-03-28","cited_by_count":2877},{"id":"1607.02533","title":"Adversarial examples in the physical world","created":"2016-07-08","cited_by_count":1769},{"id":"2003.01690","title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","created":"2020-03-03","cited_by_count":1451},{"id":"1907.07174","title":"Natural Adversarial Examples","created":"2019-07-16","cited_by_count":1408},{"id":"1511.04599","title":"DeepFool: a simple and accurate method to fool deep neural networks","created":"2015-11-14","cited_by_count":1318},{"id":"1802.00420","title":"Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples","created":"2018-02-01","cited_by_count":1268},{"id":"1905.02175","title":"Adversarial Examples Are Not Bugs, They Are Features","created":"2019-05-06","cited_by_count":953},{"id":"1802.01436","title":"Variational image compression with a scale hyperprior","created":"2018-02-01","cited_by_count":935},{"id":"1610.08401","title":"Universal adversarial perturbations","created":"2016-10-26","cited_by_count":784},{"id":"2001.03994","title":"Fast is better than free: Revisiting adversarial training","created":"2020-01-12","cited_by_count":727},{"id":"1611.01704","title":"End-to-end Optimized Image Compression","created":"2016-11-05","cited_by_count":696},{"id":"1708.06131","title":"Evasion Attacks against Machine Learning at Test Time","created":"2017-08-21","cited_by_count":689},{"id":"1708.03999","title":"ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models","created":"2017-08-14","cited_by_count":666},{"id":"2010.09670","title":"RobustBench: a standardized adversarial robustness benchmark","created":"2020-10-19","cited_by_count":661},{"id":"1711.06077","title":"The Perception-Distortion Tradeoff","created":"2017-11-16","cited_by_count":587},{"id":"2002.08347","title":"On Adaptive Attacks to Adversarial Example Defenses","created":"2020-02-19","cited_by_count":555},{"id":"1705.07263","title":"Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods","created":"2017-05-20","cited_by_count":546},{"id":"1711.00117","title":"Countering Adversarial Images using Input Transformations","created":"2017-10-31","cited_by_count":520},{"id":"2205.07460","title":"Diffusion Models for Adversarial Purification","created":"2022-05-16","cited_by_count":476},{"id":"1712.04248","title":"Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models","created":"2017-12-12","cited_by_count":462},{"id":"1902.06705","title":"On Evaluating Adversarial Robustness","created":"2019-02-18","cited_by_count":447},{"id":"2006.09965","title":"High-Fidelity Generative Image Compression","created":"2020-06-17","cited_by_count":338},{"id":"2203.10886","title":"ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding","created":"2022-03-21","cited_by_count":301},{"id":"2010.00467","title":"Bag of Tricks for Adversarial Training","created":"2020-10-01","cited_by_count":200},{"id":"1608.00853","title":"A study of the effect of JPG compression on adversarial images","created":"2016-08-02","cited_by_count":197},{"id":"2209.06950","title":"Lossy Image Compression with Conditional Diffusion Models","created":"2022-09-14","cited_by_count":170},{"id":"1811.12673","title":"ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples","created":"2018-11-30","cited_by_count":105},{"id":"2310.10325","title":"Towards image compression with perfect realism at ultra-low bitrates","created":"2023-10-16","cited_by_count":103},{"id":"1705.02900","title":"Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression","created":"2017-05-08","cited_by_count":87},{"id":"2302.14301","title":"A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking","created":"2023-02-28","cited_by_count":85},{"id":"2212.13824","title":"Multi-Realism Image Compression with a Conditional Generator","created":"2022-12-28","cited_by_count":80},{"id":"2303.01870","title":"Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models","created":"2023-03-03","cited_by_count":79},{"id":"2303.09051","title":"Robust Evaluation of Diffusion-Based Adversarial Purification","created":"2023-03-16","cited_by_count":65},{"id":"2103.01498","title":"A Survey On Universal Adversarial Attack","created":"2021-03-02","cited_by_count":59},{"id":"2312.02753","title":"C3: High-performance and low-complexity neural compression from a single image or video","created":"2023-12-05","cited_by_count":55},{"id":"1909.11515","title":"Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks","created":"2019-09-25","cited_by_count":54},{"id":"2305.18231","title":"High-Fidelity Image Compression with Score-based Generative Models","created":"2023-05-26","cited_by_count":54},{"id":"2212.05458","title":"COOL-CHIC: Coordinate-based Low Complexity Hierarchical Image Codec","created":"2022-12-11","cited_by_count":51},{"id":"2205.14501","title":"PO-ELIC: Perception-Oriented Efficient Learned Image Coding","created":"2022-05-28","cited_by_count":36},{"id":"2404.08580","title":"Lossy Image Compression with Foundation Diffusion Models","created":"2024-04-12","cited_by_count":36},{"id":"2401.08920","title":"Idempotence and Perceptual Image Compression","created":"2024-01-17","cited_by_count":29},{"id":"2110.09903","title":"Unrestricted Adversarial Attacks on ImageNet Competition","created":"2021-10-17","cited_by_count":23},{"id":"2406.05927","title":"MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification","created":"2024-06-09","cited_by_count":21},{"id":"2410.20197","title":"Transferable Adversarial Attacks on SAM and Its Downstream Models","created":"2024-10-26","cited_by_count":21},{"id":"2405.16817","title":"Controlling Rate, Distortion, and Realism: Towards a Single Comprehensive Neural Image Compression Model","created":"2024-05-27","cited_by_count":17},{"id":"2402.02263","title":"MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers","created":"2024-02-03","cited_by_count":16},{"id":"2305.14188","title":"The Best Defense is a Good Offense: Adversarial Augmentation against Adversarial Attacks","created":"2023-05-23","cited_by_count":15},{"id":"2502.02260","title":"Position: Adversarial ML for LLMs Is Not Making Any Progress","created":"2025-02-04","cited_by_count":15},{"id":"2408.17064","title":"Instant Adversarial Purification with Adversarial Consistency Distillation","created":"2024-08-30","cited_by_count":14},{"id":"2412.00505","title":"Good, Cheap, and Fast: Overfitted Image Compression with Wasserstein Distortion","created":"2024-11-30","cited_by_count":13},{"id":"2312.04960","title":"MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness","created":"2023-12-08","cited_by_count":12},{"id":"2408.05446","title":"Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness","created":"2024-08-08","cited_by_count":12},{"id":"2403.04493","title":"What makes an image realistic?","created":"2024-03-07","cited_by_count":10},{"id":"2409.08255","title":"LoRID: Low-Rank Iterative Diffusion for Adversarial Purification","created":"2024-09-12","cited_by_count":7},{"id":"1811.09831","title":"Attention, Please! Adversarial Defense via Activation Rectification and Preservation","created":"2018-11-24","cited_by_count":4},{"id":"2411.14834","title":"Evaluating the Robustness of the \"Ensemble Everything Everywhere\" Defense","created":"2024-11-22","cited_by_count":3},{"id":"2409.05558","title":"Seeing Through the Mask: Rethinking Adversarial Examples for CAPTCHAs","created":"2024-09-09","cited_by_count":2},{"id":"2410.19493","title":"Conditional Hallucinations for Image Compression","created":"2024-10-25","cited_by_count":2},{"id":"2504.12255","title":"Human Aligned Compression for Robust Models","created":"2025-04-16","cited_by_count":1}],"next_cursor":null}