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arXiv AI

Neural Network Verification for Deep Joint Source-Channel Coding

The paper introduces the first bound‑propagation framework for verifying the decoder of Deep Joint Source‑Channel Coding (DeepJSCC), providing worst‑case reconstruction error guarantees over a specified wireless channel noise region. It extends existing DNN verification techniques to handle PReLU activations, transposed convolutions, and Rayleigh fading, and incorporates Lipschitz‑regularized global robustness training (GloRo) to tighten certification bounds. Experiments on image transmission models show significant improvements in certified safety and real‑world OFDM validation confirms the theoretical guarantees.

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arXiv Computer Vision
Oct 12

Selective Depthwise Separable Convolution for Lightweight Joint Source-Channel Coding in Wireless Image Transmission

The paper introduces a lightweight joint source-channel coding framework for wireless image transmission that selectively replaces standard convolutional layers with depthwise separable convolutional layers. By varying the replacement ratio and position, the authors generate models with different computational complexities and evaluate how these changes affect reconstruction performance. Experiments show that replacing intermediate encoder and decoder layers yields a favorable complexity‑performance trade‑off, revealing redundancy in deep learning‑based JSCC systems and enabling substantial parameter reduction with only slight performance loss.

By Ming Ye, Kui Cai, Cunhua Pan, Zhen Mei, Wanting Yang, Chunguo Li
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arXiv Machine Learning
Aug 7

EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver

arXiv:2602. 11834v2 Announce Type: replace-cross Abstract: While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from poor scaling with increasing spatial multiplexing order and lack of explainability and generalization.

By Mikko Honkala, Dani Korpi, Elias Raninen, Janne M. J. Huttunen
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arXiv Machine Learning
3d ago

On the Adversarial Vulnerability of Neural Receivers with Unrolled LDPC Decoders

The paper investigates the adversarial vulnerability of neural receivers that incorporate unrolled LDPC decoders for 5G NR. It finds that standard gradient-based attacks are ineffective due to gradient masking caused by tanh saturation in the BP check-node update, leading to a dramatic decay in gradient norm over iterations. To overcome this, the authors propose two loss functions—L_BP and L_LLR—that restore attack efficacy, significantly increasing block-error rates across different neural receiver implementations.

By Aakash Saini, Jafar Aco Mohammadi, Wolfgang Gerstacker
safety
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arXiv Machine Learning
Sep 21

On the Limits of Maximal Coding Rate Reduction for Out-of-Distribution Generalisation

The paper investigates the limits of the maximal coding rate reduction (MCR²) framework for out‑of‑distribution (OOD) generalisation. It shows that MCR² can lead to complete prediction failure under distribution shift, even when a perfectly stable feature is available, and that adding invariance principles from IRM or REx does not resolve this issue. The authors conclude that additional assumptions or learning principles are needed to guarantee stable OOD predictions with MCR².

By Menghui Zhou, Gaoshan Bi, Vitaveska Lanfranchi, Po Yang
safety
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arXiv AI
Jun 18

Revealing Hidden Vulnerabilities in Autoencoders through Gradient Signal Restoration

arXiv:2505. 03646v5 Announce Type: replace-cross Abstract: Adversarial robustness of deep autoencoders (AEs) has received less attention than that of discriminative models, although their compressed latent representations induce ill-conditioned mappings that can amplify small input perturbations and destabilize reconstructions.

By Chethan Krishnamurthy Ramanaik, Arjun Roy, Tobias Callies, Eirini Ntoutsi
multimodalsafety
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