An Improved Reversible Data Hiding in Encrypted Images using Parametric Binary Tree Labeling

This work proposes an improved reversible data hiding scheme in encrypted images using parametric binary tree labeling(IPBTL-RDHEI), which takes advantage of the spatial correlation in the entire original image but not in small image blocks to reserve room for hiding data. Then the original image is encrypted with an encryption key and the parametric binary tree is used to label encrypted pixels into two different categories. Finally, one of the two categories of encrypted pixels can embed secret information by bit replacement. According to the experimental results, compared with several state-of-the-art methods, the proposed IPBTL-RDHEI method achieves higher embedding rate and outperforms the competitors. Due to the reversibility of IPBTL-RDHEI, the original plaintext image and the secret information can be restored and extracted losslessly and separately

Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows

Previous VoIP steganalysis methods face great challenges in detecting speech signals at low embedding rates, and they are also generally difficult to perform real-time detection, making them hard to truly maintain cyberspace security. To solve these two challenges, in this paper, combined with the sliding window detection algorithm and Convolution Neural Network we propose a real-time VoIP steganalysis method which based on multi-channel convolution sliding windows. In order to analyze the correlations between frames and different neighborhood frames in a VoIP signal, we define multi channel sliding detection windows. Within each sliding window, we design two feature extraction channels which contain multiple convolution layers with multiple convolution kernels each layer to extract correlation features of the input signal. Then based on these extracted features, we use a forward fully connected network for feature fusion. Finally, by analyzing the statistical distribution of these features, the discriminator will determine whether the input speech signal contains covert information or not.We designed several experiments to test the proposed model's detection ability under various conditions, including different embedding rates, different speech length, etc. Experimental results showed that the proposed model outperforms all the previous methods, especially in the case of low embedding rate, which showed state-of-the-art performance. In addition, we also tested the detection efficiency of the proposed model, and the results showed that it can achieve almost real-time detection of VoIP speech signals.Comment: 13 pages, summit to ieee transactions on information forensics and security (tifs

Reversible data hiding based on reducing invalid shifting of pixels in histogram shifting

In recent years, reversible data hiding (RDH), a new research hotspot in the field of information security, has been paid more and more attention by researchers. Most of the existing RDH schemes do not fully take it into account that natural image's texture has influence on embedding distortion. The image distortion caused by embedding data in the image's smooth region is much smaller than that in the unsmooth region, essentially, it is because embedding additional data in the smooth region corresponds to fewer invalid shifting pixels (ISPs) in histogram shifting. Thus, we propose a RDH scheme based on the images texture to reduce invalid shifting of pixels in histogram shifting. Specifically, first, a cover image is divided into two sub-images by the checkerboard pattern, and then each sub-image's fluctuation values are calculated. Finally, additional data can be embedded into the region of sub-images with smaller fluctuation value preferentially. The experimental results demonstrate that the proposed method has higher capacity and better stego-image quality than some existing RDH schemes.Comment: 11 pages, 11 figures, 1 tabl

When an attacker meets a cipher-image in 2018: A Year in Review

This paper aims to review the encountered technical contradictions when an attacker meets the cipher-images encrypted by the image encryption schemes (algorithms) proposed in 2018 from the viewpoint of an image cryptanalyst. The most representative works among them are selected and classified according to their essential structures. Almost all image cryptanalysis works published in 2018 are surveyed due to their small number. The challenging problems on design and analysis of image encryption schemes are summarized to receive the attentions of both designers and attackers (cryptanalysts) of image encryption schemes, which may promote solving scenario-oriented image security problems with new technologies.Comment: 12 page

JPEG Steganalysis Based on DenseNet

Different from the conventional deep learning work based on an images content in computer vision, deep steganalysis is an art to detect the secret information embedded in an image via deep learning, pose challenge of detection weak information invisible hidden in a host image thus learning in a very low signal-to-noise (SNR) case. In this paper, we propose a 32- layer convolutional neural Networks (CNNs) in to improve the efficiency of preprocess and reuse the features by concatenating all features from the previous layers with the same feature- map size, thus improve the flow of information and gradient. The shared features and bottleneck layers further improve the feature propagation and reduce the CNN model parameters dramatically. Experimental results on the BOSSbase, BOWS2 and ImageNet datasets have showed that the proposed CNN architecture can improve the performance and enhance the robustness. To further boost the detection accuracy, an ensemble architecture called as CNN-SCA-GFR is proposed, CNN-SCA- GFR is also the first work to combine the CNN architecture and conventional method in the JPEG domain. Experiments show that it can further lower detection errors. Compared with the state-of-the-art method XuNet [1] on BOSSbase, the proposed CNN-SCA-GFR architecture can reduce detection error rate by 5.67% for 0.1 bpnzAC and by 4.41% for 0.4 bpnzAC while the number of training parameters in CNN is only 17% of what used by XuNet. It also decreases the detection errors from the conventional method SCA-GFR by 7.89% for 0.1 bpnzAC and 8.06% for 0.4 bpnzAC, respectively.Comment: 7 pages, 4 figure

Trends toward real-time network data steganography

Network steganography has been a well-known covert data channeling method for over three decades. The basic set of techniques and implementation tools have not changed significantly since their introduction in the early 1980's. In this paper, we review the predominant methods of classical network steganography, describing the detailed operations and resultant challenges involved in embedding data in the network transport domain. We also consider the various cyber threat vectors of network steganography and point out the major differences between classical network steganography and the widely known end-point multimedia embedding techniques, which focus exclusively on static data modification for data hiding. We then challenge the security community by introducing an entirely new network dat hiding methodology, which we refer to as real-time network data steganography. Finally we provide the groundwork for this fundamental change of covert network data embedding by forming a basic framework for real-time network data operations that will open the path for even further advances in computer network security.Comment: 20 pages introducing the concept of real-time network steganograph

OR-Benchmark: An Open and Reconfigurable Digital Watermarking Benchmarking Framework

Benchmarking digital watermarking algorithms is not an easy task because different applications of digital watermarking often have very different sets of requirements and trade-offs between conflicting requirements. While there have been some general-purpose digital watermarking benchmarking systems available, they normally do not support complicated benchmarking tasks and cannot be easily reconfigured to work with different watermarking algorithms and testing conditions. In this paper, we propose OR-Benchmark, an open and highly reconfigurable general-purpose digital watermarking benchmarking framework, which has the following two key features: 1) all the interfaces are public and general enough to support all watermarking applications and benchmarking tasks we can think of; 2) end users can easily extend the functionalities and freely configure what watermarking algorithms are tested, what system components are used, how the benchmarking process runs, and what results should be produced. We implemented a prototype of this framework as a MATLAB software package and used it to benchmark a number of digital watermarking algorithms involving two types of watermarks for content authentication and self-restoration purposes. The benchmarking results demonstrated the advantages of the proposed benchmarking framework, and also gave us some useful insights about existing image authentication and self-restoration watermarking algorithms which are an important but less studied topic in digital watermarking

Calibrated Audio Steganalysis

Calibration is a common practice in image steganalysis for extracting prominent features. Based on the idea of reembedding, a new set of calibrated features for audio steganalysis applications are proposed. These features are extracted from a model that has maximum deviation from human auditory system and had been specifically designed for audio steganalysis. Ability of the proposed system is tested extensively. Simulations demonstrate that the proposed method can accurately detect the presence of hidden messages even in very low embedding rates. Proposed method achieves an accuracy of 99.3% ([email protected]% BPB) which is 9.5% higher than the previous R-MFCC based steganalysis method.Comment: 7 pages, 4 figures, 3 tables, 2016 1st International Conference on New Research Achievements in Electrical and Computer Engineering, https://en.civilica.com/Paper-CBCONF01-CBCONF01_0107=Calibrated-Audio-Steganalysis.htm

A reversible high embedding capacity data hiding technique for hiding secret data in images

As the multimedia and internet technologies are growing fast, the transmission of digital media plays an important role in communication. The various digital media like audio, video and images are being transferred through internet. There are a lot of threats for the digital data that are transferred through internet. Also, a number of security techniques have been employed to protect the data that is transferred through internet. This paper proposes a new technique for sending secret messages securely, using steganographic technique. Since the proposed system uses multiple level of security for data hiding, where the data is hidden in an image file and the stego file is again concealed in another image. Previously, the secret message is being encrypted with the encryption algorithm which ensures the achievement of high security enabled data transfer through internet.Comment: IEEE Publication format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 7 No. 3, March 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis

Steganalysis: Detecting LSB Steganographic Techniques

Steganalysis means analysis of stego images. Like cryptanalysis, steganalysis is used to detect messages often encrypted using secret key from stego images produced by steganography techniques. Recently lots of new and improved steganography techniques are developed and proposed by researchers which require robust steganalysis techniques to detect the stego images having minimum false alarm rate. This paper discusses about the different Steganalysis techniques and help to understand how, where and when this techniques can be used based on different situations.Comment: 5 pages, 1 figur