EmoAttack: Emotion-to-Image Diffusion Models for Emotional Backdoor Generation

TL;DR

This paper introduces EmoAttack, a novel backdoor attack on text-to-image diffusion models that uses emotional input to generate malicious negative content, highlighting a new security risk in AI-generated imagery.

Contribution

The paper proposes EmoBooth, a method to embed emotional triggers into diffusion models for backdoor attacks without retraining, and provides a dataset and analysis validating its effectiveness.

Findings

EmoBooth successfully triggers malicious content with emotional inputs.

The attack method avoids extensive retraining of diffusion models.

Analysis confirms the feasibility and threat of emotion-aware backdoor attacks.

Abstract

Text-to-image diffusion models can generate realistic images based on textual inputs, enabling users to convey their opinions visually through language. Meanwhile, within language, emotion plays a crucial role in expressing personal opinions in our daily lives and the inclusion of maliciously negative content can lead users astray, exacerbating negative emotions. Recognizing the success of diffusion models and the significance of emotion, we investigate a previously overlooked risk associated with text-to-image diffusion models, that is, utilizing emotion in the input texts to introduce negative content and provoke unfavorable emotions in users. Specifically, we identify a new backdoor attack, i.e., emotion-aware backdoor attack (EmoAttack), which introduces malicious negative content triggered by emotional texts during image generation. We formulate such an attack as a diffusion personalization problem to avoid extensive model retraining and propose the EmoBooth. Unlike existing personalization methods, our approach fine-tunes a pre-trained diffusion model by establishing a mapping between a cluster of emotional words and a given reference image containing malicious negative content. To validate the effectiveness of our method, we built a dataset and conducted extensive analysis and discussion about its effectiveness. Given consumers' widespread use of diffusion models, uncovering this threat is critical for society.