Adversarial Image Generation for Cross-Domain Attack Robustness
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Solution Overview
Problem
Deep neural networks (DNNs) are vulnerable to adversarial attacks, where minor perturbations in input data cause incorrect outputs, and existing methods struggle to effectively model and defend against physical adversarial attacks due to image distortion during domain conversion from digital to physical.
Innovation Solution
An adversarial attack model is trained using a generator network and discriminator network, with geometric transformations, to generate and optimize adversarial images that maintain attack effectiveness across domains, supervised by adversarial and discrimination losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adversarial attack models are trained without considering physical domain transformations, then digital domain attack effectiveness is improved, but physical domain attack effectiveness deteriorates due to image distortion
Solution Approach 1:
The patent introduces geometric transformation parameters as an additional dimension in the training process. By incorporating rotation, scaling, and other geometric transformations during training, the model learns to generate adversarial examples that are robust across different physical domain transformations, thereby improving cross-domain adaptability without sacrificing digital domain attack effectiveness
Solution Approach 2:
The patent dynamically adjusts training parameters by introducing geometric transformation variables. The training process optimizes not only the adversarial perturbation parameters but also the geometric transformation parameters, allowing the model to adapt to various physical domain conditions and maintain attack effectiveness across domains
2Object-affected harmful factors
If generator network is trained to produce highly realistic adversarial images, then attack deception capability is improved, but model complexity increases
Solution Approach 1:
The patent implements a feedback mechanism through the discriminator network that evaluates the realism of generated adversarial images. The discriminator provides gradient feedback to the generator, guiding it to produce more realistic adversarial examples while maintaining a relatively simple generator architecture, thus improving deception capability without excessive complexity increase
Solution Approach 2:
The generator network is designed to serve multiple functions: generating adversarial perturbations, applying geometric transformations, and producing final adversarial images. This multi-functionality reduces the need for separate specialized components, maintaining model complexity at acceptable levels while achieving high deception capability
3Reliability
If adversarial training incorporates multiple transformation types, then physical domain robustness is improved, but training time increases
Solution Approach 1:
The patent pre-defines a set of common geometric transformation types (rotation, scaling, flipping) that are most relevant to physical domain variations. By preparing these transformation templates in advance and incorporating them into the training pipeline, the model achieves physical domain robustness without the need for exhaustive real-time transformation exploration, thereby reducing training time
Solution Approach 2:
The patent applies a selected subset of geometric transformations during training rather than all possible transformations. This partial action approach focuses computational resources on the most impactful transformation types, achieving sufficient physical domain robustness while avoiding the exponential time cost of exhaustive transformation coverage
Data Source
AI summary
Aspects of the disclosure are directed to a training method and apparatus of an adversarial attack model, a generating method and apparatus of an adversarial image, an electronic device, and a storage medium. The adversarial attack model can include a generator network, and the training method can include using the generator network to generate an adversarial attack image based on a training digital image, and performing an adversarial attack on a target model based on the adversarial attack image, to obtain an adversarial attack result. The training method can further include obtaining a physical image corresponding to the training digital image, and training the generator network based on the training digital image, the adversarial attack image, the adversarial attack result, and the physical image.