{"total":537,"rows":[{"id":"2112.03570","title":"Membership Inference Attacks From First Principles","created":"2021-12-07","cited_by_count":878,"area":"safety"},{"id":"2103.07853","title":"Membership Inference Attacks on Machine Learning: A Survey","created":"2021-03-14","cited_by_count":321,"area":"safety"},{"id":"2109.13398","title":"Unrolling SGD: Understanding Factors Influencing Machine Unlearning","created":"2021-09-27","cited_by_count":279,"area":"safety"},{"id":"2302.09880","title":"Towards Unbounded Machine Unlearning","created":"2023-02-20","cited_by_count":268,"area":"safety"},{"id":"2111.08947","title":"Fast Yet Effective Machine Unlearning","created":"2021-11-17","cited_by_count":217,"area":"safety"},{"id":"2304.04934","title":"Model Sparsity Can Simplify Machine Unlearning","created":"2023-04-11","cited_by_count":214,"area":"safety"},{"id":"2308.07707","title":"Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening","created":"2023-08-15","cited_by_count":181,"area":"safety"},{"id":"2111.08440","title":"On the Importance of Difficulty Calibration in Membership Inference Attacks","created":"2021-11-15","cited_by_count":147,"area":"safety"},{"id":"2303.11570","title":"Boundary Unlearning","created":"2023-03-21","cited_by_count":134,"area":"safety"},{"id":"2201.05629","title":"Zero-Shot Machine Unlearning","created":"2022-01-14","cited_by_count":133,"area":"safety"},{"id":"2108.00352","title":"BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning","created":"2021-08-01","cited_by_count":123,"area":"safety"},{"id":"2203.07320","title":"The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining","created":"2022-03-14","cited_by_count":118,"area":"safety"},{"id":"2110.11794","title":"Federated Unlearning via Class-Discriminative Pruning","created":"2021-10-22","cited_by_count":115,"area":"safety"},{"id":"2312.03262","title":"Low-Cost High-Power Membership Inference Attacks","created":"2023-12-06","cited_by_count":107,"area":"safety"},{"id":"2103.15543","title":"Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models","created":"2021-03-29","cited_by_count":105,"area":"safety"},{"id":"2110.07831","title":"RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models","created":"2021-10-15","cited_by_count":104,"area":"safety"},{"id":"2106.08970","title":"Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch","created":"2021-06-16","cited_by_count":102,"area":"safety"},{"id":"2208.14933","title":"Membership Inference Attacks by Exploiting Loss Trajectory","created":"2022-08-31","cited_by_count":101,"area":"safety"},{"id":"2208.12268","title":"FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning","created":"2022-08-25","cited_by_count":91,"area":"safety"},{"id":"2403.05030","title":"Defending Against Unforeseen Failure Modes with Latent Adversarial Training","created":"2024-03-08","cited_by_count":88,"area":"safety"},{"id":"2102.02551","title":"ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models","created":"2021-02-04","cited_by_count":85,"area":"safety"},{"id":"2201.06640","title":"Towards Adversarial Evaluations for Inexact Machine Unlearning","created":"2022-01-17","cited_by_count":84,"area":"safety"},{"id":"2310.00076","title":"Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks","created":"2023-09-29","cited_by_count":81,"area":"safety"},{"id":"2101.01341","title":"Practical Blind Membership Inference Attack via Differential Comparisons","created":"2021-01-05","cited_by_count":78,"area":"safety"},{"id":"2104.09667","title":"Manipulating SGD with Data Ordering Attacks","created":"2021-04-19","cited_by_count":74,"area":"safety"},{"id":"2108.11023","title":"EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learning","created":"2021-08-25","cited_by_count":73,"area":"safety"},{"id":"2310.12560","title":"Fast Model Debias with Machine Unlearning","created":"2023-10-19","cited_by_count":72,"area":"safety"},{"id":"2406.16201","title":"Blind Baselines Beat Membership Inference Attacks for Foundation Models","created":"2024-06-23","cited_by_count":69,"area":"safety"},{"id":"2110.08324","title":"Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture","created":"2021-10-15","cited_by_count":67,"area":"safety"},{"id":"2301.11578","title":"Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers","created":"2023-01-27","cited_by_count":67,"area":"safety"},{"id":"2307.03694","title":"Scalable Membership Inference Attacks via Quantile Regression","created":"2023-07-07","cited_by_count":65,"area":"safety"},{"id":"2304.03400","title":"RoSteALS: Robust Steganography using Autoencoder Latent Space","created":"2023-04-06","cited_by_count":57,"area":"safety"},{"id":"2303.15180","title":"Detecting Backdoors in Pre-trained Encoders","created":"2023-03-23","cited_by_count":55,"area":"safety"},{"id":"2103.04980","title":"Deep Model Intellectual Property Protection via Deep Watermarking","created":"2021-03-08","cited_by_count":54,"area":"safety"},{"id":"2106.06916","title":"Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization","created":"2021-06-13","cited_by_count":52,"area":"safety"},{"id":"2202.02628","title":"Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation","created":"2022-02-05","cited_by_count":52,"area":"safety"},{"id":"2401.06187","title":"Scissorhands: Scrub Data Influence via Connection Sensitivity in Networks","created":"2024-01-11","cited_by_count":52,"area":"safety"},{"id":"2305.06360","title":"Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy","created":"2023-05-10","cited_by_count":51,"area":"safety"},{"id":"2102.10496","title":"Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits","created":"2021-02-21","cited_by_count":49,"area":"safety"},{"id":"2401.10371","title":"Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning","created":"2024-01-18","cited_by_count":49,"area":"safety"},{"id":"2112.05588","title":"Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models","created":"2021-12-10","cited_by_count":46,"area":"safety"},{"id":"2206.03693","title":"Autoregressive Perturbations for Data Poisoning","created":"2022-06-08","cited_by_count":46,"area":"safety"},{"id":"2106.14851","title":"Data Poisoning Won't Save You From Facial Recognition","created":"2021-06-28","cited_by_count":45,"area":"safety"},{"id":"2409.19732","title":"Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement","created":"2024-09-29","cited_by_count":45,"area":"safety"},{"id":"2403.07362","title":"Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning","created":"2024-03-12","cited_by_count":44,"area":"safety"},{"id":"2212.10717","title":"Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks","created":"2022-12-21","cited_by_count":43,"area":"safety"},{"id":"2110.06904","title":"Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks","created":"2021-10-13","cited_by_count":40,"area":"safety"},{"id":"2210.10750","title":"Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries","created":"2022-10-19","cited_by_count":40,"area":"safety"},{"id":"2405.04825","title":"Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature Attribution","created":"2024-05-08","cited_by_count":40,"area":"safety"},{"id":"2210.15127","title":"Rethinking the Reverse-engineering of Trojan Triggers","created":"2022-10-27","cited_by_count":39,"area":"safety"},{"id":"2312.15172","title":"Pre-trained Trojan Attacks for Visual Recognition","created":"2023-12-23","cited_by_count":39,"area":"safety"},{"id":"2410.18057","title":"CLEAR: Character Unlearning in Textual and Visual Modalities","created":"2024-10-23","cited_by_count":39,"area":"safety"},{"id":"2112.07668","title":"Dual-Key Multimodal Backdoors for Visual Question Answering","created":"2021-12-14","cited_by_count":37,"area":"safety"},{"id":"2207.05801","title":"RelaxLoss: Defending Membership Inference Attacks without Losing Utility","created":"2022-07-12","cited_by_count":37,"area":"safety"},{"id":"2304.07361","title":"PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators","created":"2023-04-14","cited_by_count":37,"area":"safety"},{"id":"2312.04095","title":"Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection","created":"2023-12-07","cited_by_count":37,"area":"safety"},{"id":"2102.04140","title":"Quantifying and Mitigating Privacy Risks of Contrastive Learning","created":"2021-02-08","cited_by_count":36,"area":"safety"},{"id":"2202.11202","title":"Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning","created":"2022-02-22","cited_by_count":34,"area":"safety"},{"id":"2205.06401","title":"PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning","created":"2022-05-13","cited_by_count":34,"area":"safety"},{"id":"2309.08230","title":"A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services","created":"2023-09-15","cited_by_count":34,"area":"safety"},{"id":"2111.07608","title":"Property Inference Attacks Against GANs","created":"2021-11-15","cited_by_count":33,"area":"safety"},{"id":"2210.08196","title":"Deep Regression Unlearning","created":"2022-10-15","cited_by_count":32,"area":"safety"},{"id":"2408.00920","title":"Towards Certified Unlearning for Deep Neural Networks","created":"2024-08-01","cited_by_count":31,"area":"safety"},{"id":"2410.22086","title":"Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate","created":"2024-10-29","cited_by_count":31,"area":"safety"},{"id":"2412.06157","title":"Membership Inference Attacks and Defenses in Federated Learning: A Survey","created":"2024-12-09","cited_by_count":31,"area":"safety"},{"id":"2211.07160","title":"FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model","created":"2022-11-14","cited_by_count":30,"area":"safety"},{"id":"2303.15127","title":"Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks","created":"2023-03-27","cited_by_count":30,"area":"safety"},{"id":"2307.08208","title":"Towards Stealthy Backdoor Attacks against Speech Recognition via Elements of Sound","created":"2023-07-17","cited_by_count":30,"area":"safety"},{"id":"2307.01610","title":"Overconfidence is a Dangerous Thing: Mitigating Membership Inference Attacks by Enforcing Less Confident Prediction","created":"2023-07-04","cited_by_count":29,"area":"safety"},{"id":"2311.02240","title":"Towards Machine Unlearning Benchmarks: Forgetting the Personal Identities in Facial Recognition Systems","created":"2023-11-03","cited_by_count":28,"area":"safety"},{"id":"2408.05868","title":"LaWa: Using Latent Space for In-Generation Image Watermarking","created":"2024-08-11","cited_by_count":28,"area":"safety"},{"id":"2503.23751","title":"Decoupled Distillation to Erase: A General Unlearning Method for Any Class-centric Tasks","created":"2025-03-31","cited_by_count":28,"area":"safety"},{"id":"2506.06985","title":"Certified Unlearning for Neural Networks","created":"2025-06-08","cited_by_count":27,"area":"safety"},{"id":"2204.09092","title":"Indiscriminate Data Poisoning Attacks on Neural Networks","created":"2022-04-19","cited_by_count":25,"area":"safety"},{"id":"2303.03592","title":"Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks","created":"2023-03-07","cited_by_count":23,"area":"safety"},{"id":"2306.07754","title":"Generative Watermarking Against Unauthorized Subject-Driven Image Synthesis","created":"2023-06-13","cited_by_count":23,"area":"safety"},{"id":"2407.15098","title":"SeqMIA: Sequential-Metric Based Membership Inference Attack","created":"2024-07-21","cited_by_count":23,"area":"safety"},{"id":"2411.07795","title":"InvisMark: Invisible and Robust Watermarking for AI-generated Image Provenance","created":"2024-11-10","cited_by_count":23,"area":"safety"},{"id":"2102.13624","title":"What Doesn't Kill You Makes You Robust(er): How to Adversarially Train against Data Poisoning","created":"2021-02-26","cited_by_count":22,"area":"safety"},{"id":"2310.18574","title":"Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning","created":"2023-10-28","cited_by_count":22,"area":"safety"},{"id":"2312.00761","title":"Deep Unlearning: Fast and Efficient Gradient-free Approach to Class Forgetting","created":"2023-12-01","cited_by_count":22,"area":"safety"},{"id":"2302.10894","title":"Red Teaming Deep Neural Networks with Feature Synthesis Tools","created":"2023-02-08","cited_by_count":21,"area":"safety"},{"id":"2306.07273","title":"Gaussian Membership Inference Privacy","created":"2023-06-12","cited_by_count":21,"area":"safety"},{"id":"2404.13588","title":"Machine Unlearning via Null Space Calibration","created":"2024-04-21","cited_by_count":21,"area":"safety"},{"id":"2408.00929","title":"Verification of Machine Unlearning is Fragile","created":"2024-08-01","cited_by_count":21,"area":"safety"},{"id":"2409.00426","title":"Is Difficulty Calibration All We Need? Towards More Practical Membership Inference Attacks","created":"2024-08-31","cited_by_count":21,"area":"safety"},{"id":"2205.13680","title":"Membership Inference Attack Using Self Influence Functions","created":"2022-05-26","cited_by_count":20,"area":"safety"},{"id":"2206.12381","title":"Defending Backdoor Attacks on Vision Transformer via Patch Processing","created":"2022-06-24","cited_by_count":20,"area":"safety"},{"id":"2303.09732","title":"Rethinking White-Box Watermarks on Deep Learning Models under Neural Structural Obfuscation","created":"2023-03-17","cited_by_count":20,"area":"safety"},{"id":"2308.09437","title":"From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space","created":"2023-08-18","cited_by_count":20,"area":"safety"},{"id":"2503.22759","title":"Data Poisoning in Deep Learning: A Survey","created":"2025-03-27","cited_by_count":20,"area":"safety"},{"id":"2105.06209","title":"DeepObliviate: A Powerful Charm for Erasing Data Residual Memory in Deep Neural Networks","created":"2021-05-13","cited_by_count":19,"area":"safety"},{"id":"2110.13541","title":"Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes","created":"2021-10-26","cited_by_count":19,"area":"safety"},{"id":"2205.15567","title":"Few-Shot Unlearning by Model Inversion","created":"2022-05-31","cited_by_count":19,"area":"safety"},{"id":"2407.10494","title":"Learning to Unlearn for Robust Machine Unlearning","created":"2024-07-15","cited_by_count":19,"area":"safety"},{"id":"2503.06339","title":"Learning to Unlearn while Retaining: Combating Gradient Conflicts in Machine Unlearning","created":"2025-03-08","cited_by_count":19,"area":"safety"},{"id":"2205.14055","title":"A Blessing of Dimensionality in Membership Inference through Regularization","created":"2022-05-27","cited_by_count":18,"area":"safety"},{"id":"2404.12922","title":"Is Retain Set All You Need in Machine Unlearning? Restoring Performance of Unlearned Models with Out-Of-Distribution Images","created":"2024-04-19","cited_by_count":18,"area":"safety"},{"id":"2503.06991","title":"Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols","created":"2025-03-10","cited_by_count":18,"area":"safety"},{"id":"2102.05257","title":"Robust Federated Learning with Attack-Adaptive Aggregation","created":"2021-02-10","cited_by_count":17,"area":"safety"}],"next_cursor":"100"}