Augmenting Adversarial Attacks: Enhancing Text-to-Image Synthesis with Self-Augmentation

Deep learning models have revolutionized image-text retrieval, enabling us to search for images based on their textual descriptions and vice versa. However, these models are vulnerable to adversarial attacks, which can manipulate the model’s predictions by introducing subtle changes in the input data. In this article, we explore the effectiveness of these attacks on image-text retrieval tasks and introduce a novel method to generate adversarial texts that can fool the model into retrieving misleading results.
To demonstrate the power of our approach, we conducted experiments on two popular datasets: Flickr30K and COCO. Our results show that our adversarial texts can successfully manipulate the model’s predictions, causing it to retrieve images that are not relevant to the input text. We also observed that our method is effective in generating adversarial images that can fool the model into retrieving misleading results.
Our work has important implications for the field of computer vision and natural language processing. Adversarial attacks can undermine the reliability of image-text retrieval models, which are increasingly being used in real-world applications. By developing effective methods to generate adversarial texts and images, we can improve the robustness of these models and prevent malicious attacks.
In summary, this article provides a comprehensive overview of adversarial attacks on deep learning models for image-text retrieval and introduces a novel method to generate adversarial texts that can manipulate the model’s predictions. Our experimental results demonstrate the effectiveness of our approach and highlight the importance of developing robust models against adversarial attacks.