Exploiting the Natural Attack Capability of Diffusion Models: A Large-Scale Evaluation of Adversarial Attacks on Text-to-Image Generation

핵심 개념

Diffusion models can generate images that bypass object detectors by removing robust visual features, posing a new security threat.

초록

The paper investigates the "natural attack capability" of state-of-the-art text-to-image diffusion models, where simple text prompts can guide the models to generate images that bypass object detectors while remaining stealthy to humans.
The key highlights are:

The authors identify a new type of attack, called the Natural Denoising Diffusion (NDD) attack, which exploits the natural attack capability of diffusion models. The NDD attack can generate low-cost, model-agnostic, and transferable adversarial attacks by removing robust visual features like shape, color, text, and pattern from the generated images.

To systematically evaluate the natural attack capability, the authors construct a large-scale dataset called the Natural Denoising Diffusion Attack (NDDA) dataset, covering various combinations of removing robust features for three object classes: stop sign, fire hydrant, and horse.

Experiments on the NDDA dataset show that popular object detectors can still recognize the objects in the generated images, even when the robust features are intentionally removed. For example, 32% of the stop sign images without any robust features are still detected as stop signs.

A user study confirms the high stealthiness of the NDD attack - the stop sign images generated by altering the "STOP" text have an 88% detection rate against object detectors, while 93% of human subjects do not recognize them as stop signs.

The authors find that the non-robust features embedded by diffusion models play a significant role in enabling the natural attack capability, as demonstrated by comparing normal and "robustified" classifiers.

To validate the real-world applicability, the authors demonstrate the model-agnostic and transferable attack capability of the NDD attack against a commodity autonomous driving vehicle, where 73% of the printed attacks are detected as stop signs.

The study highlights the security risks introduced by the powerful image generation capabilities of diffusion models and calls for further research to develop robust defenses.

통계

The stop sign images generated by DALL-E 2 with all robust features removed are still detected as stop signs by YOLOv3, YOLOv5, DETR, Faster R-CNN, and RTMDet with an average detection rate of 6%.
The stop sign images generated by Stable Diffusion 2 with all robust features removed are still detected as stop signs by the object detectors with an average detection rate of 28%.
The stop sign images generated by Deepfloyd IF with all robust features removed are still detected as stop signs by the object detectors with an average detection rate of 32%.

인용구

"We identify a new type of attack, called the Natural Denoising Diffusion (NDD) attack based on the finding that state-of-the-art deep neural network (DNN) models still hold their prediction even if we intentionally remove their robust features, which are essential to the human visual system (HVS), by text prompts."
"The NDD attack can generate low-cost, model-agnostic, and transferrable adversarial attacks by exploiting the natural attack capability in diffusion models."
"We find that the non-robust features embedded by diffusion models contribute to the natural attack capability."

핵심 통찰 요약

Intriguing Properties of Diffusion Models: An Empirical Study of the Natural Attack Capability in Text-to-Image Generative Models

by Takami Sato,... 게시일 arxiv.org 05-03-2024

https://arxiv.org/pdf/2308.15692.pdf

더 깊은 질문

How can we develop robust object detection models that are resilient to the natural attack capability of diffusion models?

To develop robust object detection models that can withstand the natural attack capability of diffusion models, several strategies can be employed:

Adversarial Training: Incorporating adversarial training techniques can help improve the robustness of object detection models. By exposing the model to adversarial examples during training, it can learn to better generalize and resist attacks.

Feature Diversity: Ensuring that the object detection model relies on a diverse set of features for classification can make it more resilient to attacks that target specific non-robust features generated by diffusion models.

Ensemble Methods: Utilizing ensemble methods by combining multiple object detection models can enhance robustness. By aggregating predictions from different models, the system can better handle attacks that may fool individual models.

Regularization Techniques: Applying regularization techniques such as dropout, weight decay, or data augmentation can help prevent overfitting and improve the model's generalization ability, making it more resistant to attacks.

Adaptive Defense Mechanisms: Implementing adaptive defense mechanisms that can detect and respond to adversarial attacks in real-time can be effective. These mechanisms can monitor model behavior and trigger alerts or countermeasures when suspicious activity is detected.

What other security and privacy risks might emerge from the widespread adoption of powerful text-to-image generation models?

The widespread adoption of powerful text-to-image generation models, such as diffusion models, may introduce several security and privacy risks:

Forgery and Misinformation: These models can be used to create highly realistic fake images and videos, leading to the spread of misinformation, fake news, and forged content.

Privacy Violations: Generated images may contain sensitive or private information inadvertently leaked by the model, posing privacy risks to individuals or organizations.

Impersonation Attacks: Malicious actors could use text-to-image models to create convincing fake identities or impersonate others, leading to identity theft or fraud.

Bias and Discrimination: If not properly trained and monitored, text-to-image models can perpetuate biases present in the training data, leading to discriminatory outcomes in generated content.

Intellectual Property Infringement: The ease of generating high-quality images with these models may facilitate intellectual property theft or copyright infringement.

Security Vulnerabilities: Text-to-image models themselves may be vulnerable to attacks, such as adversarial examples or model inversion attacks, compromising the integrity and security of the generated content.

Could the natural attack capability of diffusion models be leveraged for beneficial applications, such as automated testing of computer vision systems?

The natural attack capability of diffusion models, as demonstrated in the context provided, could indeed be leveraged for beneficial applications, such as automated testing of computer vision systems. Some potential use cases include:

Robustness Testing: Using diffusion models to generate challenging, non-robust images can help evaluate the robustness of object detection systems and identify vulnerabilities that need to be addressed.

Adversarial Defense Training: By exposing computer vision systems to natural attacks generated by diffusion models, developers can train their models to be more resilient to adversarial attacks in real-world scenarios.

Quality Assurance: Automated testing using natural attack capabilities can ensure the reliability and accuracy of computer vision systems by simulating real-world challenges and edge cases.

Security Auditing: Employing diffusion models for automated security auditing can help organizations proactively identify weaknesses in their computer vision systems and implement necessary safeguards.

Data Augmentation: Generating diverse and challenging images through natural attacks can serve as a form of data augmentation, enhancing the generalization and performance of computer vision models.

Overall, leveraging the natural attack capability of diffusion models for beneficial applications can contribute to the development of more robust and secure computer vision systems.