#deep-learning-security News & Analysis

3 articles tagged with #deep-learning-security. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBearisharXiv – CS AI · May 127/10

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Control Your View: High-Resolution Global Semantic Manipulation in Learned Image Compression

Researchers have developed PGD²-GSM, a novel adversarial attack method that successfully performs high-resolution global semantic manipulation on learned image compression systems for the first time. The breakthrough uses a Periodic Geometric Decay schedule to overcome limitations in existing attack methods, exposing a critical vulnerability in DNN-based compression systems that previous techniques could not achieve.

AINeutralarXiv – CS AI · Jun 26/10

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CEAR: Certified Ensemble Adversarial Robustness in DNNs

Researchers propose CEAR, an ensemble-based defense mechanism combining empirical and certified robustness techniques to protect deep neural networks against adversarial attacks. The method uses varying Gaussian noise, temperature adjustments, and novel voting mechanisms while extending randomized smoothing to ensemble classifiers, demonstrating improved certified accuracy across benchmark datasets.

AIBearisharXiv – CS AI · May 46/10

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BadSNN: Backdoor Attacks on Spiking Neural Networks via Adversarial Spiking Neuron

Researchers have developed BadSNN, a novel backdoor attack method targeting Spiking Neural Networks by exploiting hyperparameter variations in spiking neurons. The attack demonstrates superior performance compared to existing backdoor methods and shows resistance to current mitigation techniques, raising security concerns for SNNs used in edge computing and neuromorphic applications.