AEGIS: A Semantic GAN and Evidential Learning Frameworkfor Robust Adversarial Detection in Vision Sensors
arXiv:2606.28416v1 Announce Type: cross Abstract: Deep neural networks (DNNs) have shown outstanding performance in visual recognition tasks within vision sensor networks; however, they are still vulnerable to adversarial manipulations and imperceptible perturbations that can lead to erroneous predictions. To address that, this paper presents AEGIS, a semantic aware and uncertainty guided adversarial detection framework designed for robust image classification in vision sensors pipelines. At its core, a SemantiGAN module functions as a multi class semantic discriminator, identifying and filter
The proliferation of deep neural networks in critical vision systems necessitates robust detection methods against growing adversarial threats, making this solution timely.
This development enhances the security and reliability of AI systems in vision sensors, which are crucial for applications ranging from autonomous vehicles to defense.
Vision-based AI systems can now incorporate a more sophisticated defense against adversarial attacks, improving their trustworthiness and deployment potential.
- · AI cybersecurity firms
- · Autonomous vehicle developers
- · Defense contractors
- · Industrial automation
- · Adversarial attackers
- · Developers of unsecure AI models
Increased resilience of visual AI applications against manipulation.
Faster adoption of AI in sensitive environments where robust security is paramount.
Reduced regulatory hurdles for AI deployment due to improved safety and security assurances.
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Read at arXiv cs.AI