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New 'Counterfeit Answers' attack targets OCR-free DocVQA models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel adversarial attack method called "Counterfeit Answers" that can forge document content to manipulate OCR-free Document Visual Question Answering (DocVQA) models. This attack can induce specific incorrect answers or cause systematic model failures by creating visually imperceptible yet semantically targeted forged documents. The effectiveness of this attack was demonstrated against state-of-the-art models like Pix2Struct and Donut, highlighting significant vulnerabilities in current DocVQA systems and the need for improved defenses. AI

IMPACT Highlights critical vulnerabilities in DocVQA systems, necessitating the development of more robust defenses against adversarial attacks.

RANK_REASON Research paper detailing a new adversarial attack method against AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New 'Counterfeit Answers' attack targets OCR-free DocVQA models

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Research paper detailing a new adversarial attack method against AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Marco Pintore, Maura Pintor, Dimosthenis Karatzas, Battista Biggio ·

    Counterfeit Answers: Adversarial Forgery against OCR-Free Document Visual Question Answering

    arXiv:2512.04554v2 Announce Type: replace Abstract: Document Visual Question Answering (DocVQA) enables end-to-end reasoning grounded on information present in a document input. While recent models have shown impressive capabilities, they remain vulnerable to adversarial attacks.…

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