Deep Equilibrium Gradients for Diffusion Model Adversarial Attacks
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Solution Overview
Problem
Existing methods for calculating gradients in diffusion models are memory-prohibitive and yield imprecise results, leading to sub-optimal adversarial attacks and overestimating the robustness of diffusion models, which are used to defend against adversarial attacks.
Innovation Solution
Employ a deep equilibrium root solver to approximate the gradients of the denoising process in diffusion models, allowing for more denoising steps without excessive memory usage, using a fixed-point equation and Anderson acceleration solvers to improve gradient calculation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If backpropagation method is used to calculate gradients of the noising-denoising process, then gradient calculation is straightforward, but memory usage becomes prohibitive
Solution Approach 1:
The gradient calculation process is segmented into two independent parts: (1) forward pass through the diffusion model to generate denoised image, and (2) separate gradient computation using the generated image and ground truth label. This segmentation allows memory-efficient processing by avoiding the need to store all intermediate activations for backpropagation through the entire noising-denoising process.
Solution Approach 2:
The patent introduces an intermediary computation step where the denoised image is generated first, then used as input for gradient calculation alongside the ground truth label. This intermediary approach decouples the forward pass from the gradient computation, enabling memory-efficient adversarial attack without requiring full backpropagation through the diffusion model's iterative process.
2Quantity of substance
If SDE solver is used to approximate gradients, then memory usage is reduced, but gradient precision deteriorates leading to sub-optimal adversarial attacks
Solution Approach 1:
The patent replaces the stochastic differential equation solver approach with a direct gradient computation method using the generated denoised image and ground truth label. This substitution eliminates the approximation errors inherent in SDE solvers while maintaining memory efficiency, as the gradient is computed directly from the relationship between the denoised image, ground truth, and loss function rather than through iterative stochastic approximation.
3Reliability
If more denoising steps are performed, then robustness of diffusion model improves, but computational complexity increases
Solution Approach 1:
The patent performs the complete forward pass through all denoising steps to generate the final denoised image, but only computes gradients for the adversarial attack on this final output. This partial action approach allows the diffusion model to benefit from multiple denoising steps for robustness while avoiding the computational burden of computing gradients through each intermediate step, as the adversarial perturbation is applied to the final denoised image using the ground truth label.
Data Source
AI summary
A method for attacking a neural network that includes receiving an input data that includes an image and ground truth label, adding a pre-determined amount of noise to the image, denoising the noisy image utilizing a diffusion model that includes a deep equilibrium root solver, determining a first gradient of the denoised image with respect to the input data including at least the image, wherein the first gradient is associated with the diffusion model, utilizing the denoised image at downstream model, outputting a predicated label associated with the denoised image, determining a loss utilizing with the predicted label and the ground truth label, determining a second gradient associated with the downstream model utilizing at least the loss, and outputting an aggregate gradient that represents an error of the neural network output utilizing the predicted label, wherein the aggregate gradient is calculated utilizing the first gradient and the second gradient.