NeurIPS 2024 Past Other
The Third Workshop on New Frontiers in Adversarial Machine Learning
AdvML-Frontiers 2024
- Submission deadline
- Aug 31, 2024, 13:05 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (37)
Fetched from OpenReview (v2) on 2026-06-10.
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Achieving Domain-Independent Certified Robustness via Knowledge Continuity
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AdjointDEIS: Efficient Gradients for Diffusion Models
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Advancing NLP Security by Leveraging LLMs as Adversarial Engines
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Adversarial Bounding Boxes Generation (ABBG) Attack against Visual Object Trackers
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Adversarial Databases Improve Success in Retrieval-based Large Language Models
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Adversarial Training based Domain Adaptation for Cross-Subject Emotion Recognition
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Adversarial Watermarking for Face Recognition
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An Adversarial Learning Approach to Irregular Time-Series Forecasting
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Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?
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Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute Manipulations
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dSTAR: Straggler Tolerant and Byzantine Resilient Distributed SGD
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Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness
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Hiding-in-Plain-Sight (HiPS) Attack on CLIP for Targetted Object Removal from Images
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Imitation Guided Automated Red Teaming
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In Search of the $\textit{Successful}$ Interpolation: On the Role of $\textit{Sharpness}$ in CLIP Generalization
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In-distribution adversarial attacks on object recognition models using gradient-free search.
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Jailbreak Defense in a Narrow Domain: Failures of existing methods and Improving Transcript-Based Classifiers
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Learning From Convolution-based Unlearnable Datasets
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Learning to Forget using Hypernetworks
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LLM-PIRATE: A benchmark for indirect prompt injection attacks in Large Language Models
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Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference
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Moral Persuasion in Large Language Models: Evaluating Susceptibility and Ethical Alignment
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Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
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RenderAttack: Hundreds of Adversarial Attacks Through Differentiable Texture Generation
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Rethinking Backdoor Detection Evaluation for Language Models
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Rethinking Randomized Smoothing from the Perspective of Scalability
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Robustness of Practical Perceptual Hashing Algorithms to Hash-Evasion and Hash-Inversion Attacks
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SkipOOD: Efficient Out-of-Distribution Input Detection using Skipping Mechanism
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Smoothing-Based Adversarial Defense Methods for Inverse Problems
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Sparse patches adversarial attacks via extrapolating point-wise information
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Sparse Transfer Learning Accelerates and Enhances Certified Robustness: A Comprehensive Study
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The Ultimate Cookbook for Invisible Poison: Crafting Subtle Clean-Label Text Backdoors with Style Attributes
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Track 1: Robust Offline Learning via Adversarial World Models
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TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers
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Unveiling Synthetic Faces: How Synthetic Datasets Can Expose Real Identities
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vTune: Verifiable Fine-Tuning Through Backdooring
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When Do Universal Image Jailbreaks Transfer Between Vision-Language Models?