arXiv subjects

Maria Slim

Publications and source records attributed to Maria Slim.

4 recordsLinked to original sources

arXiv2026

Channel-Adaptive Region Adjacency Graph Carriers for Semantic Image Communication

Semantic image communication seeks to preserve task-relevant scene structure under limited channel resources, but carriers are often dense latent tensors or grid-aligned semantic layouts that do not explicitly encode region-level relations. This work introduces a segmentation-derived region adjacency graph (RAG) carrier, termed channel-adaptive RAG (CA-RAG), for joint source-channel coding-style image communication. Nodes store interpretable region attributes, edges preserve adjacency, channel-adaptive graph simplification (CGS) controls the node budget, and semantic belief propagation refines noisy graph embeddings before diffusion-based reconstruction. On Cityscapes, pre-channel RAG payloads are several times smaller than compressed class-index layouts in a 2,000-image study. Under additive white Gaussian noise at signal-to-noise ratios from 0 to 15 dB, CA-RAG reports higher semantic consistency than deep joint source-channel coding and a same-decoder layout baseline, with comparable perceptual quality. At 10 dB, the full-budget rate-sweep point reaches mean intersection over union (mIoU) = 0.329 at approximately 3.3 x 10^3 channel uses, while the default adaptive-CGS setting reports mIoU = 0.294 at approximately 2.6 x 10^3 channel uses.

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arXiv2026

Where to Defend? Layer-Wise Adversarial Training for Robust Transformer-Based Semantic Communications

Deep learning-based semantic communication (DeepSC), a Transformer-based encoder-decoder, achieves semantic fidelity over noisy channels but remains vulnerable to adversarial perturbations injected at multiple stages of the pipeline. We present a layer-wise robustness framework that compares fast gradient sign method (FGSM), projected gradient descent (PGD), and l2-normalized fast gradient method (FGM) defenses at the embedding output, and then uses PGD to analyze three attack and defense points: the embedding output, encoder output, and channel-encoder bottleneck. We evaluate reconstruction on Europarl and UK Hansard and sentiment classification on SST2 and YELP under additive white Gaussian noise (AWGN) and Rayleigh fading. A first-order damage budget, {epsilon} times the l1 norm of the clean-input loss gradient at each injection point, predicts the attack-severity ordering, and the transfer matrix reveals asymmetric defense transfer: the embedding defense transfers strongly to encoder attacks, whereas encoder defenses degrade robustness against upstream attacks. For reconstruction, encoder-point training yields the largest matched gain but fails severely under embedding attacks; joint embedding-plus-encoder training retains comparable gains under encoder attacks while mitigating this mismatch failure, and Rayleigh fading attenuates robustness gains and degradation. For classification, three of four defenses collapse to constant predictors; only the channel-encoder defense remains non-degenerate, suggesting a protective role for the 128D-to-16D bottleneck. At a signal-to-noise ratio (SNR) of 9 dB and perturbation budget {epsilon} = 0.3, the matched encoder defense recovers bilingual evaluation understudy (BLEU) from ~ 0.10 to ~ 0.64 on Europarl/AWGN, whereas the mismatched encoder defense yields a -0.444 BLEU change relative to the undefended baseline under an embedding attack.

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arXiv2026

Privacy-Preserving Deep Joint Source-Channel Coding with In-Loop Concept Erasure

Deep joint source-channel coding (DeepJSCC) transmits learned semantic features efficiently but can leak sensitive attributes such as gender, race, or speaker identity. We propose LEAPSC (LEACE-in-the-loop privacy for semantic communication), whose core contribution is the integration of in-loop least-squares concept erasure (LEACE) within a variational information bottleneck (VIB) encoder. By periodically refitting the projection operator during training, LEAPSC couples the encoder dynamics to the erasure mechanism, driving attribute-conditional mean differences toward zero within each task-label group on the fitting sample. Additional components, namely conditional value-at-risk (CVaR) tail-sensitive privacy, feature-wise linear modulation (FiLM) signal-to-noise ratio conditioning, and Lagrangian dual ascent, improve robustness across channel conditions and over the high-leakage tail of samples. On CelebA, FairFace, and Google Speech Commands, LEAPSC reaches task accuracy of 0.862, 0.755, and 0.925 respectively, with attacker accuracy at or below the label-only floor on CelebA (0.548 vs. floor 0.580) and within 2 percentage points (pp) of chance elsewhere, improving over an information-bottleneck adversarial baseline (IBAL) at a matched 52-epoch budget by +3.6, +2.5, and +1.3 pp (Welch's t-test, p=0.019 on CelebA).

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arXiv2026

Krum-Inspired Central Teacher Selection and Residual Channel Bottlenecks for Efficient DeepSC

Deploying transformer-based semantic communication models on edge devices requires compression that preserves semantic fidelity under channel variability. We study a compressed deep semantic communication (DeepSC) student trained by multi-teacher knowledge distillation and combine two ideas: (i) a residual channel bottleneck that splits the transmitted representation into a base stream and a residual stream with unequal power allocation, and (ii) a Krum-inspired, medoid-style centrality criterion that selects a single central teacher from a five-model ensemble, applied at the logit and intermediate-feature levels and combined with a feature-dominant distillation loss. On the EuroParl benchmark, a two-layer student recovers about 98% of the four-layer teacher's bilingual evaluation understudy (BLEU)-1 under additive white Gaussian noise, with 93% of its BLEU-4 and 96% of its sentence-BERT (SBERT) score, and about 89%, 77%, and 87% of the teacher's BLEU-1, BLEU-4, and SBERT, respectively, under Rayleigh fading, while reducing non-embedding parameters by 1.33x and single-teacher inference latency by 1.79x (9.0x relative to a five-teacher ensemble used here as an upper-bound reference, not a deployment baseline). Controlled ablations over three seeds indicate complementary contributions of about +8.4% BLEU-1 (+9.6% SBERT) from the residual bottleneck and +3.0% (+2.9% SBERT) from centrality-based selection over mean aggregation. A decoder-mode ablation shows that the base stream alone recovers about two-thirds of the full BLEU-1 while the residual stream alone collapses, supporting the role of the residual as a refinement on top of the base.

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