ChipGuard-AI: A Hardware-Assisted Unified Framework for Adversarial and Side-Channel Resilience in Consumer Electronics

Consumer electronics powered by on-device artificial intelligence face a structurally coupled dual threat: adversarial attacks that manipulate model outputs, and side-channel attacks that exploit physical implementation characteristics to extract cryptographic keys and neural network weights. Prior work addresses these threats independently, leaving exploitable gaps when both attack vectors are applied in a coordinated manner. This paper presents ChipGuard-AI, a hardware-assisted unified security framework that co-designs adversarial input detection, multi-modal side-channel countermeasures, and Physical Unclonable Function-rooted device authentication on a shared cross-domain security substrate. A dedicated Security Processing Unit maintains a cross-domain security state vector in real time, enabling coordinated hardware-accelerated response to concurrent multi-vector attacks with 2.3 ms adversarial detection latency. A Deep Q-Network adaptive policy dynamically allocates countermeasures to balance security strength against resource overhead. Experimental validation on CIFAR-10 adversarial benchmarks and ChipWhisperer physical side-channel traces demonstrates 97.3% adversarial detection accuracy and 89.7% side-channel leakage reduction. Field-programmable gate array synthesis on Xilinx Zynq UltraScale+ achieves these security levels at 12.4% computational overhead and 6.8% power increase. The adaptive reinforcement learning policy achieves 11.3% lower average overhead than threshold heuristics while maintaining equivalent security effectiveness. Multi-platform validation across four consumer electronics device classes, from ultra-low-power Internet of Things endpoints to flagship smartphone system-on-chips, demonstrates 91.2% to 95.7% adversarial detection and 76.4% to 85.3% side-channel protection with 8.3% to 14.1% computational overhead.