相关论文: Off-By-One Implementation Error in J-UNIWARD

Image hiding is often referred to as steganography, which aims to hide a secret image in a cover image of the same resolution. Many steganography models are based on genera-tive adversarial networks (GANs) and variational autoencoders…

The Jaccard index, also known as Intersection-over-Union (IoU), is one of the most critical evaluation metrics in image semantic segmentation. However, direct optimization of IoU score is very difficult because the learning objective is…

Steganography is about how to send secret message covertly. And the purpose of steganalysis is to not only detect the existence of the hidden message but also extract it. So far there have been many reliable detecting methods on various…

This paper introduces a novel compatibility attack to detect a steganographic message embedded in the DCT domain of a JPEG image at high-quality factors (close to 100). Because the JPEG compression is not a surjective function, i.e. not…

Image Steganography is a cryptographic technique that embeds secret information into an image, ensuring the hidden data remains undetectable to the human eye while preserving the image's original visual integrity. Least Significant Bit…

The joint bidiagonalization (JBD) process iteratively reduces a matrix pair $\{A,L\}$ to two bidiagonal forms simultaneously, which can be used for computing a partial generalized singular value decomposition (GSVD) of $\{A,L\}$. The…

Steganography represents the art of unobtrusively concealing a secrete message within some cover data. The key scope of this work is about visual steganography techniques that hide a full-sized color image / video within another. A majority…

Image steganography refers to the process of hiding information inside images. Steganalysis is the process of detecting a steganographic image. We introduce a steganalysis approach that uses an ensemble color space model to obtain a…

Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators. Existing methods fuse multiple annotations using a simple voting process, ignoring the inherent ambiguity of edges and…

As a branch of reversible data hiding (RDH), reversible data hiding in JEPG is particularly important. Because JPEG images are widely used, it is great significance to study reversible data hiding algorithm for JEPG images. The existing…

This short paper proposes to use the statistical analysis of the correlation between DCT coefficients to design a new synchronization strategy that can be used for cost-based steganographic schemes in the JPEG domain. First, an analysis is…

The needs for steganographic techniques for hiding secret message inside images have been arise. This paper is to create a practical steganographic implementation to hide text inside grey scale images. The secret message is hidden inside…

Despite recent strides made by AI in image processing, the issue of mixed exposure, pivotal in many real-world scenarios like surveillance and photography, remains inadequately addressed. Traditional image enhancement techniques and current…

Steganography involves hiding a secret message or image inside another cover image. Changes are made in the cover image without affecting visual quality of the image. In contrast to cryptography, Steganography provides complete secrecy of…

Learned image compression possesses a unique challenge when incorporating non-differentiable quantization into the gradient-based training of the networks. Several quantization surrogates have been proposed to fulfill the training, but they…

It is widely recognized that the image format is crucial to steganography for that each individual format has its unique properities. Nowadays, the most famous approach of digital image steganography is to combine a well-defined distortion…

Network steganography encompasses the information hiding techniques that can be applied in communication network environments and that utilize hidden data carriers for this purpose. In this paper we introduce a characteristic called…

Depth-aware video panoptic segmentation is a promising approach to camera based scene understanding. However, the current state-of-the-art methods require costly video annotations and use a complex training pipeline compared to their…

Recent study has found out that after multiple times of recompression, the DCT coefficients of JPEG image can form an embedding domain that is robust to recompression, which is called transport channel matching (TCM) method. Because the…

Unsupervised depth completion and estimation methods are trained by minimizing reconstruction error. Block artifacts from resampling, intensity saturation, and occlusions are amongst the many undesirable by-products of common data…