Evaluating the Semantic-to-Geometric Gap in Adversarial Defenses Against Vision-Language Model-Based Plagiarism

arXiv:2609.26733 · cs.CV · Submitted 2026-09-22 · Read on arXiv

cs.CV

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/christopherburger/AMVT

Terminology

Consistency — 11× in this paper · explained in 3 episodes
Accuracy — 6× in this paper · explained in 1 episode
Robustness — 6× in this paper · explained in 5 episodes
Adversarial Defense — 4× in this paper · explained in 1 episode
Adversarial Examples — 4× in this paper · explained in 2 episodes
Alignment — 3× in this paper · explained in 1 episode
Transformer — 3× in this paper · explained in 2 episodes
Ablation — 2× in this paper · explained in 1 episode
Benchmark — 2× in this paper · explained in 2 episodes
Interpretability — 2× in this paper · explained in 15 episodes
Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
Ablation Studies — 1× in this paper · explained in 1 episode
Ablation study — 1× in this paper · explained in 1 episode
Adversarial Attacks — 1× in this paper · explained in 1 episode
Benchmarking — 1× in this paper · explained in 3 episodes
Fidelity — 1× in this paper · explained in 2 episodes
Generative AI — 1× in this paper · explained in 2 episodes
Personalization — 1× in this paper · explained in 2 episodes
Trace — 1× in this paper · explained in 2 episodes
Transformer Models — 1× in this paper · explained in 2 episodes
Verification — 1× in this paper · explained in 2 episodes

Sources

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