GeoDetect: Geometric Adversarial Detection for VLPs
Merged summary
TL;DR - GeoDetect is a method for detecting adversarial examples against vision-language pre-trained models (VLPs) by exploiting geometric properties of their embedding spaces, addressing a gap where prior detection work focused on single-modality models.
- Analyzes VLP embedding geometry and finds structured anisotropy distinct from unimodal vision models.
- Provides theoretical analysis showing adversarial examples get pushed off-manifold, increasing their expected geometric distance to randomly sampled points versus clean inputs.
- Proposes GeoDetect, which uses geometric scores capturing these off-manifold deviations to flag adversarial examples.
- Reports reliable detection across diverse VLP architectures and threat settings, including unimodal, multimodal, and adaptive attacks (specific metrics not provided in the abstract).
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GeoDetect: Geometric Adversarial Detection for VLPs
TL;DR - GeoDetect is a method for detecting adversarial examples against vision-language pre-trained models (VLPs) by exploiting geometric properties of their embedding spaces, addressing a gap where prior detection work focused on single-modality models.
- Analyzes VLP embedding geometry and finds structured anisotropy distinct from unimodal vision models.
- Provides theoretical analysis showing adversarial examples get pushed off-manifold, increasing their expected geometric distance to randomly sampled points versus clean inputs.
- Proposes GeoDetect, which uses geometric scores capturing these off-manifold deviations to flag adversarial examples.
- Reports reliable detection across diverse VLP architectures and threat settings, including unimodal, multimodal, and adaptive attacks (specific metrics not provided in the abstract).