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Search: “adversarial perturbation”

4 stories in the last 7d

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Epsilon-Nash Equilibria in History-Dependent SA-MDPs

Researchers give the first algorithm for computing epsilon-approximate history-dependent equilibria in state-adversarial Markov decision processes with observation-perturbing adversaries.

The paper studies state-adversarial Markov decision processes (SA-MDPs) where an adversary knowing the true state perturbs observations within state-dependent proximity sets each step. The authors prove universal history-dependent equilibrium policies do not exist and reduce SA-MDPs to a strategically equivalent constrained zero-sum one-sided partially observable stochastic game, enabling the first algorithmic route to epsilon-approximations of initial-state dependent equilibria. The algorithm is validated on small analytically verifiable games and scales to larger benchmarks, including Atari Freeway rollouts with a 12-period-ahead horizon.

arXiv cs.CR · 19h agoAI safety & security

A Unified and Constrained View of Regularization-Based Robust Reinforcement Learning

Paper unifies regularization-based robust RL methods via new performance-gap upper bounds and jointly learned Lagrange multipliers.

The authors derive new upper bounds on the gap between nominal and worst-case deep RL policies, each expressible as an existing regularization objective plus a KL-divergence penalty. Robust training is reformulated as constrained optimization, where prior methods correspond to a fixed Lagrange multiplier. The multiplier is instead updated jointly with the policy, auto-tuning the regularization weight. Adversarial evaluations across several continuous control tasks validate the theory.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research1

Robust Policy Optimization via Adversarial Importance Sampling

Adversarial Importance Sampling estimates worst-case RL returns without extra interactions; authors also release the advrl PyTorch library.

The paper introduces Advis, which uses importance sampling over trajectories from standard training to estimate and optimize verifiable worst-case returns, requiring no additional environment interactions or auxiliary networks. It also releases advrl, a modular PyTorch library of single-file robustness methods and adversarial attacks for reproducible evaluation. The authors show adversarial hyperparameters do not transfer across agents, so they evaluate with 6-14x more attacker configurations than prior work. Effectiveness is demonstrated on continuous control environments.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research1

RAPID: A Real-Time Defense Against Unauthorized Model Distillation for Text-to-Image Services

RAPID embeds defensive perturbations in a T2I model's shared VAE decoder to block unauthorized black-box distillation in real time.

The paper defends text-to-image services against model theft via black-box output-based distillation, where adversaries collect prompt-image pairs to train substitute models. RAPID integrates defensive perturbations into the shared VAE decoder using self-referenced latent maximization plus reconstruction-guided color regularization, avoiding costly sample-wise online optimization. Across four T2I models and four datasets versus five baselines, it consistently degrades substitute-model generation quality while preserving visual fidelity.

arXiv cs.CR · 2d agoAI safety & security