arXiv Machine Learning
Aug 11
Targeted Label-Flipping and Oversampling Attacks on Federated Conditional GANs
arXiv:2608. 09314v1 Announce Type: new Abstract: In a federated learning setup for GANs, several adversarial attacks are possible.
Targeted Label-Flipping and Oversampling Attacks on Federated Conditional GANs
In a federated learning setup for GANs, several adversarial attacks are possible. One such attack is label flipping, in which malicious clients deliberately alter label information during local training in order to manipulate the global generator.
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arXiv AI
Jul 16
UTS at ELOQUENT 2026 Voight-Kampff: structural shifts in AI writing bypass state-of-the-art detectors
arXiv:2607. 13565v1 Announce Type: cross Abstract: We investigate which language model evasion attacks survive state-of-the-art adversarial fine-tuning, developing strategies that sweep the top 5 positions on the ELOQUENT 2026 Voight-Kampff leaderboard.
arXiv Machine Learning
2d ago
Localization then Neutralization: Gradient-guided Token Suppression against Visual Prompt Injection Attack
arXiv:2605. 25194v2 Announce Type: replace Abstract: Adversarial images pose a severe security threat to multimodal large language models through prompt injection.
arXiv Machine Learning
Aug 3
Low-Cost Hard-Label Adversarial Attack with Theoretical Foundations
arXiv:2601. 14300v4 Announce Type: replace Abstract: Hard-label black-box attacks, relying solely on top-1 predictions, represent one of the most challenging yet practically threat models.
arXiv Machine Learning
Jul 8
Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization
arXiv:2508. 01725v5 Announce Type: replace Abstract: Recent advances in continuous conditional generative modeling, including Continuous conditional Generative Adversarial Network (CcGAN) and Continuous Conditional Diffusion Model (CCDM), estimate high-dimensional data distributions conditioned on scalar regression labels such as angles, ages, or temperatures.
Hugging Face Trending Papers
Aug 10
Confusion-Geometry Rebalancing for Long-Tailed Adversarial Training
Adversarial training under long tailed distributions suffers from a dual imbalance: the class imbalance skews the training objective toward head classes, and the adversarial inner maximization may further amplify this bias. Existing methods mitigate this issue by correcting class priors or adapting class wise robust supervision, yet they treat each class in isolation and fail to identify which boundaries drive long tailed collapse.
arXiv AI
Jul 3
Population-Based Multi-Objective Training of Discriminators for Semi-Supervised GANs
arXiv:2607. 01907v1 Announce Type: cross Abstract: Semi-supervised generative adversarial networks (SSL-GANs) can exploit large unlabeled datasets while retaining a classifier in the discriminator, but their training is often unstable.
arXiv AI
Jun 4
Efficient Adversarial Attacks on High-dimensional Offline Bandits
arXiv:2602. 01658v2 Announce Type: replace-cross Abstract: Bandit algorithms have recently emerged as a powerful tool for evaluating machine learning models, including generative image models and large language models, by efficiently identifying top-performing candidates without exhaustive comparisons.
arXiv AI
Jul 7
A Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning
arXiv:2607. 03600v1 Announce Type: cross Abstract: Adversarial robustness in Unsupervised Domain Adaptation (UDA) remains a significant challenge due to noisy pseudo labels and inherent distributional shifts between the clean source and adversarially perturbed target domains.
arXiv AI
Jun 9
The Confidence Trap: Calibration Attacks for Graph Neural Networks
arXiv:2606. 08467v1 Announce Type: cross Abstract: While confidence calibration is essential for trustworthy decision-making in safety-critical applications, the robustness of calibrated GNNs to adversarial structural perturbations remains largely unexplored.
arXiv Machine Learning
Jun 2
Adversarially Robust Control of Conditional Value-at-Risk via Rockafellar-Uryasev Conformal Inference
arXiv:2606. 00320v1 Announce Type: new Abstract: We present an online, distribution-free framework for controlling the Conditional Value-at-Risk (CVaR), extending conformal tail risk control to non-stationary and adversarial environments.
arXiv Machine Learning
5d ago
GENADA: efficient generative time series adversarial attack framework
arXiv:2608. 12535v1 Announce Type: new Abstract: Deep learning models are widely used for time series analysis in domains such as healthcare, finance, energy systems, and environmental monitoring.