Towards Reversible De-Identification in Video Sequences Using 3D Avatars and Steganography

Deep Learning for Predictive Analytics in Reversible Steganography

Deep learning is regarded as a promising solution for reversible steganography. There is an accelerating trend of representing a reversible steo-system by monolithic neural networks, which bypass intermediate operations in traditional pipelines of reversible steganography. This end-to-end paradigm, however, suffers from imperfect reversibility. By contrast, the modular paradigm that incorporates neural networks into modules of traditional pipelines can stably guarantee reversibility with mathematical explainability. Prediction-error modulation is a well-established reversible steganography pip…

Bayesian Neural Networks for Reversible Steganography

Recent advances in deep learning have led to a paradigm shift in the field of reversible steganography. A fundamental pillar of reversible steganography is predictive modelling which can be realised via deep neural networks. However, non-trivial errors exist in inferences about some out-of-distribution and noisy data. In view of this issue, we propose to consider uncertainty in predictive models based upon a theoretical framework of Bayesian deep learning, thereby creating an adaptive steganographic system. Most modern deep-learning models are regarded as deterministic because they only offer …

On the predictability in reversible steganography

Artificial neural networks have advanced the frontiers of reversible steganography. The core strength of neural networks is the ability to render accurate predictions for a bewildering variety of data. Residual modulation is recognised as the most advanced reversible steganographic algorithm for digital images. The pivot of this algorithm is predictive analytics in which pixel intensities are predicted given some pixel-wise contextual information. This task can be perceived as a low-level vision problem and hence neural networks for addressing a similar class of problems can be deployed. On to…

Automation of reversible steganographic coding with nonlinear discrete optimisation

Authentication mechanisms are at the forefront of defending the world from various types of cybercrime. Steganography can serve as an authentication solution through the use of a digital signature embedded in a carrier object to ensure the integrity of the object and simultaneously lighten the burden of metadata management. Nevertheless, despite being generally imperceptible to human sensory systems, any degree of steganographic distortion might be inadmissible in fidelity-sensitive situations such as forensic science, legal proceedings, medical diagnosis and military reconnaissance. This has …

StyleStegan：基于特征隐写的无泄漏风格迁移

StyleStegan: Leak-free Style Transfer Based on Feature Steganography

针对风格迁移中的内容泄漏问题，提出基于特征隐写的无泄漏方法StyleStegan，结合神经流模型与隐写术实现无损无偏风格化及内容特征隐藏，有效缓解串行与可逆任务中的泄漏问题，SSIM指标显著提升。

SMILENet：通过协同马赛克可逆隐藏网络释放超大容量图像隐写术

SMILENet: Unleashing Extra-Large Capacity Image Steganography via a Synergistic Mosaic InvertibLE Hiding Network

SMILENet通过协同可逆与不可逆模块架构及统一训练策略，将图像隐写容量提升至25张图像，并引入容量-失真权衡新指标，在多个数据集上全面超越现有方法。