Neural Network Verification for Deep Joint Source-Channel Coding
Deep joint source-channel coding (DeepJSCC) transmits data end-to-end over wireless channels using a neural encoder-decoder, but reconstruction quality can degrade sharply under adversarial perturbations and channel disturbances; no method formally bounds this degradation for DeepJSCC. We present the first bound-propagation framework for verifying DeepJSCC's decoder, bounding worst-case reconstruction error over a given wireless channel's noise region. Current deep neural network (DNN) verifiers do not support three DeepJSCC decoder components: parametric rectified linear activations (PReLU), transposed convolutions, and Rayleigh fading. We extend state-of-the-art techniques for optimizat...
| Authors | Thanh Le, Hai Duong, Takeshi Matsumura et al. |
|---|---|
| Primary category | cs.SE |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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