From the 1 of 4 linked papers with an AI index.
4 papers
Driving up Inference Energy on SNNs: Per-Sample and Universal Sponge Attacks
Spyridon Raptis, Haralampos-G. Stratigopoulos
The paper demonstrates that spiking neural networks, which are energy‑efficient on neuromorphic hardware, can be targeted by adversarial "sponge" attacks that increase spike activi…
Stealing AI Model Weights Through Covert Communication Channels
Valentin Barbaza, Alan Rodrigo Diaz-Rizo, Hassan Aboushady +2
AI models are often regarded as valuable intellectual property due to the high cost of their development, the competitive advantage they provide, and the proprietary techniques inv…
Input-Specific and Universal Adversarial Attack Generation for Spiking Neural Networks in the Spiking Domain
Spyridon Raptis, Haralampos-G. Stratigopoulos
As Spiking Neural Networks (SNNs) gain traction across various applications, understanding their security vulnerabilities becomes increasingly important. In this work, we focus on…
Input-Triggered Hardware Trojan Attack on Spiking Neural Networks
Spyridon Raptis, Paul Kling, Ioannis Kaskampas +2
Neuromorphic computing based on spiking neural networks (SNNs) is emerging as a promising alternative to traditional artificial neural networks (ANNs), offering unique advantages i…