2 papers
cs.CR2026
Breaking TinyML: Why Quantized Neural Networks Need Domain-Specific Security Analysis
Jacob Huckelberry, Andrea Mattia Garavagno, Yuke Zhang +3
Most TinyML hardware accelerators focus on supporting Quantized Neural Networks (QNNs) to meet stringent constraints on power consumption and size. Despite this, the security aspec…
cs.CV2026
NeVStereo: A NeRF-Driven NVS-Stereo Architecture for High-Fidelity 3D Tasks
Pengcheng Chen, Yue Hu, Wenhao Li +5
In modern dense 3D reconstruction, feed-forward systems (e.g., VGGT, pi3) focus on end-to-end matching and geometry prediction but do not explicitly output the novel view synthesis…