Adversarial Attack
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Benchmarks
Most implemented
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Evaluating the Robustness of Neural Networks
Universal and Transferable Adversarial Attacks on Aligned Language Models
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
The Limitations of Deep Learning in Adversarial Settings
Papers
Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks
Existing global optimization benchmark suites are of a moderate size and are based on a small number of analytical functions that date back even to the 1970s. This causes a risk of biasing the development of global optim…
Adversarial AttackUniversal Concept Disruption for SAM3 Image Segmentation
SAM3 extends promptable segmentation from geometry-driven mask prediction to open-vocabulary concept segmentation, where a text-conditioned grounding model decides whether a concept is present and segments all matching i…
Adversarial RobustnessImage SegmentationAdversarial AttackMissClick: Exploiting Digit-Serialized Coordinates to Attack GUI Grounding Models
Recent GUI visual grounding models generate screen coordinates as sequences of digit tokens that are parsed into numerical values and mapped to executable clicks. The security implications of this coordinate generation p…
Adversarial AttackVisual GroundingAdversarial Deepfake Generation and an Investigation of Purification-Based Adversarial Detection
This paper describes the participation of team "Go To Germany" in the ImageCLEF 2026 Deepfake Detection and Generation Task. For the image generation task, we employ FLUX.1-dev with PuLID for identity-preserving face syn…
DeepFake DetectionAdversarial AttackImage GenerationGeoThreat: Transferable Targeted Adversarial Attacks on Large Vision-Language Models for Remote Sensing Image Interpretation
Adversarial attacks against large vision-language models (LVLMs) serve as an effective means of assessing their robustness in cross-modal semantic understanding. Existing studies mainly focus on corrupting visual inputs …
Adversarial AttackGuardianAgentBench: Where Agents Fail and How to Guard Them
As large language model agents increasingly operate autonomously with access to tools and external environments, ensuring their safe and reliable behavior becomes critical. We present GuardianAgentBench (GABench), a benc…
Adversarial Attack