惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

P
Proofpoint News Feed
T
The Blog of Author Tim Ferriss
aimingoo的专栏
aimingoo的专栏
M
MIT News - Artificial intelligence
N
Netflix TechBlog - Medium
Y
Y Combinator Blog
B
Blog RSS Feed
H
Help Net Security
Blog — PlanetScale
Blog — PlanetScale
Vercel News
Vercel News
Google DeepMind News
Google DeepMind News
Microsoft Security Blog
Microsoft Security Blog
G
Google Developers Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - 司徒正美
L
LangChain Blog
IT之家
IT之家
F
Fortinet All Blogs
V
V2EX
C
Check Point Blog
The Cloudflare Blog
博客园_首页
阮一峰的网络日志
阮一峰的网络日志
A
About on SuperTechFans

cs.IT updates on arXiv.org

Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Pixel-Sensitive and Robust Steganography Based on Polar C...
Yujun Ji, Jinsheng Li, Ling Liu, Qi Cao, Tao Dai · 2025-06-09 · via cs.IT updates on arXiv.org

Steganography is an information hiding technique for covert communication. The core issue in steganography design is the rate-distortion coding problem. Polar codes, which have been proven to achieve the rate-distortion bound for any binary symmetric source, are utilized to design a steganographic scheme that can reach the embedding capacity for the Distortion-Limited Sender problem in certain cases. In adaptive steganography, for attack scenarios where each noise element can have different intensities, existing steganographic coding methods fail to resist such attacks. In this paper, we propose a pixel-sensitive and robust steganographic scheme based on polar codes. Our steganographic scheme not only matches the adaptive distortion well but is also robust against sophisticated noise attacks. Futher, it is proven that our scheme achieves the embedding capacity in certain cases. Experimentally, a steganographic scheme can be designed and implemented with a secret message error rate at the $10^{-5}$ level when the attack noise is known to both the sender and the receiver. This demonstrates its significant robustness.