{"list":"other","total":60,"rows":[{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"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":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"1608.06993","title":"Densely Connected Convolutional Networks","created":"2016-08-25","cited_by_count":6720},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"2301.12597","title":"BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models","created":"2023-01-30","cited_by_count":6137},{"id":"1801.04381","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","created":"2018-01-13","cited_by_count":4795},{"id":"1709.01507","title":"Squeeze-and-Excitation Networks","created":"2017-09-05","cited_by_count":4789},{"id":"1411.7766","title":"Deep Learning Face Attributes in the Wild","created":"2014-11-28","cited_by_count":3693},{"id":"1703.04730","title":"Understanding Black-box Predictions via Influence Functions","created":"2017-03-14","cited_by_count":1741},{"id":"1708.06733","title":"BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain","created":"2017-08-22","cited_by_count":1106},{"id":"1712.05526","title":"Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning","created":"2017-12-15","cited_by_count":1055},{"id":"2103.17239","title":"Going deeper with Image Transformers","created":"2021-03-31","cited_by_count":764},{"id":"1804.00792","title":"Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks","created":"2018-04-03","cited_by_count":519},{"id":"1811.00636","title":"Spectral Signatures in Backdoor Attacks","created":"2018-11-01","cited_by_count":484},{"id":"2205.07460","title":"Diffusion Models for Adversarial Purification","created":"2022-05-16","cited_by_count":476},{"id":"1411.7923","title":"Learning Face Representation from Scratch","created":"2014-11-28","cited_by_count":453},{"id":"1910.00033","title":"Hidden Trigger Backdoor Attacks","created":"2019-09-30","cited_by_count":371},{"id":"1805.06605","title":"Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models","created":"2018-05-17","cited_by_count":362},{"id":"2302.04222","title":"Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models","created":"2023-02-08","cited_by_count":205},{"id":"1708.08689","title":"Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization","created":"2017-08-29","cited_by_count":197},{"id":"2101.04898","title":"Unlearnable Examples: Making Personal Data Unexploitable","created":"2021-01-13","cited_by_count":193},{"id":"2309.16948","title":"Denoising Diffusion Bridge Models","created":"2023-09-29","cited_by_count":178},{"id":"2302.04578","title":"Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples","created":"2023-02-09","cited_by_count":167},{"id":"1811.00741","title":"Stronger Data Poisoning Attacks Break Data Sanitization Defenses","created":"2018-11-02","cited_by_count":135},{"id":"2106.06041","title":"Adversarial purification with Score-based generative models","created":"2021-06-11","cited_by_count":134},{"id":"1905.05897","title":"Transferable Clean-Label Poisoning Attacks on Deep Neural Nets","created":"2019-05-15","cited_by_count":131},{"id":"2106.10807","title":"Adversarial Examples Make Strong Poisons","created":"2021-06-21","cited_by_count":119},{"id":"2310.13828","title":"Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models","created":"2023-10-20","cited_by_count":75},{"id":"1905.09027","title":"Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder","created":"2019-05-22","cited_by_count":70},{"id":"2005.00191","title":"Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability","created":"2020-05-01","cited_by_count":66},{"id":"1901.01677","title":"Image Super-Resolution as a Defense Against Adversarial Attacks","created":"2019-01-07","cited_by_count":65},{"id":"2111.00898","title":"Availability Attacks Create Shortcuts","created":"2021-11-01","cited_by_count":62},{"id":"2102.04716","title":"Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training","created":"2021-02-09","cited_by_count":59},{"id":"2101.09387","title":"Online Adversarial Purification based on Self-Supervision","created":"2021-01-23","cited_by_count":46},{"id":"2206.03693","title":"Autoregressive Perturbations for Data Poisoning","created":"2022-06-08","cited_by_count":46},{"id":"2106.14851","title":"Data Poisoning Won't Save You From Facial Recognition","created":"2021-06-28","cited_by_count":45},{"id":"2210.10114","title":"Transferable Unlearnable Examples","created":"2022-10-18","cited_by_count":43},{"id":"2301.13838","title":"Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression","created":"2023-01-31","cited_by_count":39},{"id":"2312.00084","title":"Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion?","created":"2023-11-30","cited_by_count":38},{"id":"2406.12027","title":"Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI","created":"2024-06-17","cited_by_count":38},{"id":"2301.01217","title":"Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples","created":"2022-12-31","cited_by_count":36},{"id":"2310.19248","title":"IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI","created":"2023-10-30","cited_by_count":35},{"id":"2202.11202","title":"Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning","created":"2022-02-22","cited_by_count":34},{"id":"2303.15127","title":"Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks","created":"2023-03-27","cited_by_count":30},{"id":"2205.12141","title":"One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks","created":"2022-05-24","cited_by_count":29},{"id":"2204.09092","title":"Indiscriminate Data Poisoning Attacks on Neural Networks","created":"2022-04-19","cited_by_count":25},{"id":"2303.03592","title":"Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks","created":"2023-03-07","cited_by_count":23},{"id":"2211.12005","title":"Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors","created":"2022-11-22","cited_by_count":19},{"id":"2303.08500","title":"The Devil's Advocate: Shattering the Illusion of Unexploitable Data using Diffusion Models","created":"2023-03-15","cited_by_count":19},{"id":"2405.01460","title":"Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders","created":"2024-05-02","cited_by_count":18},{"id":"2305.09241","title":"Unlearnable Examples Give a False Sense of Security: Piercing through Unexploitable Data with Learnable Examples","created":"2023-05-16","cited_by_count":17},{"id":"2312.08898","title":"Detection and Defense of Unlearnable Examples","created":"2023-12-14","cited_by_count":13},{"id":"2401.17523","title":"Game-Theoretic Unlearnable Example Generator","created":"2024-01-31","cited_by_count":12},{"id":"2402.04010","title":"Efficient Availability Attacks against Supervised and Contrastive Learning Simultaneously","created":"2024-02-06","cited_by_count":12},{"id":"2404.13320","title":"Pixel is a Barrier: Diffusion Models Are More Adversarially Robust Than We Think","created":"2024-04-20","cited_by_count":10},{"id":"2402.12626","title":"Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors","created":"2024-02-20","cited_by_count":8},{"id":"2405.18627","title":"PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics","created":"2024-05-28","cited_by_count":3}],"next_cursor":null}