Efficient generation of adversarial examples and cross-channel quality optimization based on latent space distribution search
Year: 2026
Type: article
Abstract: In response to the adversarial vulnerabilities of deep vision algorithms, existing security assessment methods often face problems such as low generation efficiency and severe visual distortion. It is urgent to explore a new paradigm for adversarial sample generation that takes into account high concealment and computational power economy. To this end, this paper aims to overcome the limitations of pixel-level iterative computation capabilities, eliminate biases in biological visual perception, and construct an adversarial example generation model based on latent space distribution search and cross-channel optimization techniques. This method uses a multi-layer convolutional auto-encoder as the base for dimensionality reduction, and maps the image matrix to a low-dimensional feature space for distribution search to extract adversarial priors. In terms of cross-channel quality optimization, by combining the human cone cell spectral sensing threshold and edge contour detection mask, perturbation isolation and precise location across color channels are implemented, and the counter-perturbation is covertly redistributed to visual low-sensitivity areas and high-frequency redundant areas. Experimental evaluation shows that this model maintains excellent visual fidelity while significantly reducing computing power overhead. The PSNR of adversarial samples reaches a maximum of 40.2 decibels. When the perturbation budget is increased to a standardized $$L_{\infty }$$ norm constraint of 20/255, the perceptual distance metric remains at a low of 0.125. In actual black-box detection, the model’s migration penetration rate for the large unknown residual visual model reaches 78.5%, resulting in a sharp 88.0% drop in the median target prediction confidence. This paper not only provides a high-throughput automated vulnerability investigation tool for ubiquitous artificial intelligence vision applications, but also establishes a solid theoretical foundation for cross-channel physical isolation for robust boundary reconstruction of future defense systems.
Source: Scientific Reports
Author: Yaoqin Sun
Institution: Zhejiang Agricultural Business College
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7170801924
FWCI:
Cites:
Cited by:
Related to: