ALLUDE: A Unified Evaluation System for Configurable Attacks in Differentiable Environments

• URL: http://arxiv.org/abs/2607.17077v1
• Date: Sun, 19 Jul 2026 05:02:58 GMT
• Title: ALLUDE: A Unified Evaluation System for Configurable Attacks in Differentiable Environments
• Abstract summary: Adrial attacks against vision models like object detectors are often evaluated under limited conditions, leaving their performance under-characterized.<n>We present ALLUDE, which offers first-of-its-kind evaluation capabilities across Linux and Windows.
• Score: 57.11645440673911
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adversarial attacks against vision models like object detectors are often evaluated under limited conditions, leaving their performance under-characterized. Bridging simulation and differentiable rendering enables more robust, end-to-end evaluation of these adversarial attacks, yet there is no easy-to-use, unified system that offers a rich set of customizable configurations for adversarial attacks across multiple scenes, objects, environmental and lighting conditions, and camera trajectories. We present ALLUDE, which addresses these gaps, offering first-of-its-kind evaluation capabilities across Linux and Windows. We comprehensively demonstrate ALLUDE's evaluation breadth through a two-pronged strategy: (1) using Latin Hypercube Sampling, we draw a representative subset from 5,400 configurations spanning 10 scene-object pairs, 9 weather conditions, 4 optimizers, 5 camera trajectories, and 3 detection models; (2) we stress-test existing attacks (CAMOU, RAUCA, FCA) under diverse weather conditions and continuous camera trajectories, revealing degradation of attack success across every attack, exposing evaluation gaps in prior work. Through ALLUDE's end-to-end differentiable rendering, adversarial attacks can be optimized against shifting real-world deployment conditions. Our cross-platform code is open source.

Related papers

• AttackVLA: Benchmarking Adversarial and Backdoor Attacks on Vision-Language-Action Models [60.39655329875822]
  Vision-Language-Action (VLA) models enable robots to interpret natural-language instructions and perform diverse tasks.<n>Despite growing interest in attacking such models, the effectiveness of existing techniques remains unclear.<n>We propose AttackVLA, a unified framework that aligns with the VLA development lifecycle.
  arXiv  Detail & Related papers  (2025-11-15T10:30:46Z)
• Seeing Isn't Believing: Context-Aware Adversarial Patch Synthesis via Conditional GAN [2.02409171087469]
  We introduce a novel framework for fully controllable adversarial patch generation.<n>The attacker can freely choose both the input image x and the target class y target, thereby dictating the exact misclassification outcome.<n>Our method combines a generative U-Net design with Grad-CAM-guided patch placement, enabling semantic-aware localization.
  arXiv  Detail & Related papers  (2025-09-26T18:39:21Z)
• Universal Camouflage Attack on Vision-Language Models for Autonomous Driving [67.34987318443761]
  Visual language modeling for automated driving is emerging as a promising research direction.<n>VLM-AD remains vulnerable to serious security threats from adversarial attacks.<n>We propose the first Universal Camouflage Attack framework for VLM-AD.
  arXiv  Detail & Related papers  (2025-09-24T14:52:01Z)
• Pixel-Optimization-Free Patch Attack on Stereo Depth Estimation [37.97727884936262]
  We build a unified framework that extends pixel-optimization attacks to four stereo-matching stages.<n>We propose PatchHunter, the first pixel-optimization-free attack.<n>On KITTI, PatchHunter outperforms pixel-level attacks in both effectiveness and black-box transferability.
  arXiv  Detail & Related papers  (2025-06-21T08:23:02Z)
• AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples [26.37278338032268]
  Adrial examples are typically optimized with gradient-based attacks.<n>Each is shown to outperform its predecessors using different experimental setups.<n>This provides overly-optimistic and even biased evaluations.
  arXiv  Detail & Related papers  (2024-04-30T11:19:05Z)
• To Make Yourself Invisible with Adversarial Semantic Contours [47.755808439588094]
  Adversarial Semantic Contour (ASC) is an estimate of a Bayesian formulation of sparse attack with a deceived prior of object contour. We show that ASC can corrupt the prediction of 9 modern detectors with different architectures. We conclude with cautions about contour being the common weakness of object detectors with various architecture.
  arXiv  Detail & Related papers  (2023-03-01T07:22:39Z)
• GLOW: Global Layout Aware Attacks for Object Detection [27.46902978168904]
  Adversarial attacks aim to perturb images such that a predictor outputs incorrect results. We present first approach that copes with various attack requests by generating global layout-aware adversarial attacks. In experiment, we design multiple types of attack requests and validate our ideas on MS validation set.
  arXiv  Detail & Related papers  (2023-02-27T22:01:34Z)
• CARBEN: Composite Adversarial Robustness Benchmark [70.05004034081377]
  This paper demonstrates how composite adversarial attack (CAA) affects the resulting image. It provides real-time inferences of different models, which will facilitate users' configuration of the parameters of the attack level. A leaderboard to benchmark adversarial robustness against CAA is also introduced.
  arXiv  Detail & Related papers  (2022-07-16T01:08:44Z)
• Zero-Query Transfer Attacks on Context-Aware Object Detectors [95.18656036716972]
  Adversarial attacks perturb images such that a deep neural network produces incorrect classification results. A promising approach to defend against adversarial attacks on natural multi-object scenes is to impose a context-consistency check. We present the first approach for generating context-consistent adversarial attacks that can evade the context-consistency check.
  arXiv  Detail & Related papers  (2022-03-29T04:33:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.