Recent advances in deep learning research have shown remarkable achievements across many tasks in computer vision (CV) and natural language processing (NLP). At the intersection of CV and NLP is the problem of image captioning, where the related models' robustness against adversarial attacks has not been well studied. In this paper, we present a novel adversarial attack strategy, which we call AICAttack (Attention-based Image Captioning Attack), designed to attack image captioning models through subtle perturbations on images. Operating within a black-box attack scenario, our algorithm requires no access to the target model's architecture, parameters, or gradient information. We introduce an attention-based candidate selection mechanism that identifies the optimal pixels to attack, followed by Differential Evolution (DE) for perturbing pixels' RGB values. We demonstrate AICAttack's effectiveness through extensive experiments on benchmark datasets with multiple victim models. The experimental results demonstrate that our method surpasses current leading-edge techniques by effectively distributing the alignment and semantics of words in the output.

通过对图像进行微小扰动，本论文提出了一种名为AICAttack（基于注意力的图像字幕攻击）的新型对抗攻击策略，旨在攻击图像字幕模型。通过引入基于注意力的候选选择机制和微分进化（DE），我们的算法在黑盒攻击的场景中操作，无需访问目标模型的架构、参数或梯度信息，并通过在多个受害模型上的基准数据集上进行的大量实验证明了AICAttack的有效性，实验结果表明我们的方法在输出的单词对齐和语义方面超越了目前的领先技术。

AICAttack: 基于注意力优化的对抗性图像描述攻击