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Adversarial discriminant attack on text-to-image diffusion models.

Hanxiao Wu¹, Shengwu Xiong², Dong Yi³

¹School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, Hubei, China; Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 101408, China; Wuhan AI Research, Wuhan, 430000, Hubei, China.

Neural Networks : the Official Journal of the International Neural Network Society

|February 18, 2026

Summary

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This summary is machine-generated.

A new attack method, Adversarial Discriminant Attack (ADAtk), effectively bypasses safety mechanisms in concept-erased diffusion models. ADAtk generates images classified as Not-Safe-For-Work (NSFW) with over 90% success, revealing vulnerabilities in current AI safety techniques.

Area of Science:

Artificial Intelligence
Computer Vision
Generative Models

Background:

Concept-erased diffusion models face challenges in preventing Not-Safe-For-Work (NSFW) content generation.
Existing attack methods focus on image similarity, which does not guarantee successful NSFW reconstruction.

Purpose of the Study:

To propose a novel attack method, Adversarial Discriminant Attack (ADAtk), to expose vulnerabilities in concept-erased diffusion models.
To address the limitations of existing generation-focused attacks by adopting a discriminative approach.

Main Methods:

ADAtk optimizes the likelihood of generating NSFW content by creating adversarial perturbations in the model's latent space.
The method guides image reconstruction towards a target discriminant class, aiming for classification as inappropriate.

Keywords:

AI security Adversarial discriminant attack Concept-erased diffusion model Text-to-image generation

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Last Updated: May 5, 2026

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

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Published on: August 14, 2019

Main Results:

ADAtk achieved over a 90% success rate in bypassing current internal security mechanisms.
The attack successfully exposed critical limitations in existing concept-erasure techniques for diffusion models.

Conclusions:

ADAtk provides crucial insights into improving the safety and reliability of text-to-image generation systems.
The findings pave the way for developing more secure generative AI models and robust safety protocols.