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Survey details adversarial attacks targeting Vision Transformer efficiency

By PulseAugur Editorial · [2 sources] ·

A new survey paper examines adversarial attacks that degrade the efficiency of Vision Transformers (ViTs) by exploiting their input-adaptive inference mechanisms. These attacks aim to increase computational load without significantly impacting accuracy. The paper compares two such attacks, SlowFormer and DeSparsify, across various token-pruning frameworks like A-ViT, ATS, and AdaViT, using metrics such as GFLOPs, accuracy loss, and Attack Success rate. Understanding these vulnerabilities is crucial for developing lightweight countermeasures for deployment in resource-constrained environments. AI

IMPACT Highlights vulnerabilities in efficient AI model inference, necessitating robust defenses for secure deployment.

RANK_REASON The cluster contains a survey paper detailing research on adversarial attacks against Vision Transformers.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Survey details adversarial attacks targeting Vision Transformer efficiency

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The cluster contains a survey paper detailing research on adversarial attacks against Vision Transformers.
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52 days old
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization

    Vision Transformers (ViTs) increasingly rely on input-adaptive inference, such as token pruning and early halting, to meet energy and latency budgets. This survey examines a recent class of adversarial efficiency degradation attacks that target these mechanisms to increase comput…

  2. arXiv cs.CV TIER_1 English(EN) · Anadi Goyal, Nandish Chattopadhyay, Anupam Chattopadhyay, Chandan Karfa ·

    A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization

    arXiv:2608.05217v1 Announce Type: cross Abstract: Vision Transformers (ViTs) increasingly rely on input-adaptive inference, such as token pruning and early halting, to meet energy and latency budgets. This survey examines a recent class of adversarial efficiency degradation attac…

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