Aug 2026· International Conference on Optoelectronic Information and Computer Engineering (OICE)· Vol 14317, pp. 1431717 - 1431717-13· 0 citations· 40 references
Engineering
TL;DR
LatentAdv, an adversarial camouflage generation framework that integrates ControlNet-based reference style generation and variational autoencoder (VAE) latent space optimization, is proposed, which provides a feasible solution for balancing attack performance and visual naturalness in adversarial object detection.
Adversarial Robustness with Manifold-Oriented Training (ARMOR), a novel defense that realizes the core insights of on-manifold adversarial training (OMAT) in low-data regimes and translates insights from manifold-based training to defend object detectors amidst training data scarcity.
Hao-Ran Wang, Matthew Lau, Alec Helbling et al.· 0 citations
This work proposes an efficient and effective adversarial attack detection scheme leveraging the multi-task perception within a complex vision system, and develops a consistency score metric to measure the inconsistency between vision tasks.
Cong Chen, J. Monteuuis, Jonathan Petit· 0 citations
The Enhanced Deformation Attack (EDA), which estimates attack gradients over stochastically transformed views of the current adversarial image, achieves the highest mean attack success rate (ASR) on ViT targets for all four CNN sources, outperforming the strongest source-specific baseline.
A novel sparse adversarial patch attack algorithm (Sparse Patch Attack, SPA), which successfully misleads the text generation results of VLMs by generating highly dispersed discrete pixel perturbations in non-salient regions of the image.
T.-Y. Chen, X.-Y. Hu, J.-F. Wang et al.· Advanced Electromagnetics· 0 citations
AdROD outperforms five baseline defenses and exhibits superior generalizability compared with the evaluated adversarial-training baselines, while maintaining real-time performance for safely stopping the vehicle at a stop sign instrumented with adversarial patches.
Yuting Wu, Dongfang Guo, Xiangzhong Luo et al.· 0 citations
This paper reveals that samples generated by a well-trained generative model are close to clean ones but far from adversarial ones, and proposes Consistency Model-based Adversarial Purification (CMAP), which optimizes vectors within the latent space of a pre-trained consistency model to generate samples for restoring c...
Shuhai Zhang, Jiahao Yang, Hui Luo et al.· IEEE Transactions on Pattern...· 0 citations
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