Research on Embedding Capacity and Efficiency of Information Hiding Based on Digital Images

Generally speaking, being an efficient information hiding scheme, what we want to achieve is high embedding capacity of the cover image and high visual quality of the stego image, high visual quality is also called embedding efficiency. This paper mainly studies on the information hiding technology based on gray-scale digital images and especially considers the improvement of embedding capacity and embedding efficiency. For the purpose of that, two algorithms for information hiding were proposed, one is called high capacity of information hiding algorithm (HCIH for short), which achieves high embedding rate, and the other is called high quality of information hiding algorithm (HQIH for short), which realizes high embedding efficiency. The simulation experiments show that our proposed algorithms achieve better performance.

Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence

This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission.The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission.The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data.This process effectively enhances the concealment and imperceptibility of confidential information,thereby improving the security of such information during transmission and reducing security risks.Furthermore,we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical environments.Through adversarial training,the algorithm is strengthened to enhance its resilience against attacks and overall robustness,ensuring better protection against potential threats.Experimental results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding capacity.Additionally,it ensures the quality and fidelity of the stego image.The method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information.

Reversible Data Hiding Algorithm in Encrypted Images Based on Adaptive Median Edge Detection and Ciphertext-Policy Attribute-Based Encryption

With the rapid advancement of cloud computing technology,reversible data hiding algorithms in encrypted images(RDH-EI)have developed into an important field of study concentrated on safeguarding privacy in distributed cloud environments.However,existing algorithms often suffer from low embedding capacities and are inadequate for complex data access scenarios.To address these challenges,this paper proposes a novel reversible data hiding algorithm in encrypted images based on adaptive median edge detection(AMED)and ciphertext-policy attributebased encryption(CP-ABE).This proposed algorithm enhances the conventional median edge detection(MED)by incorporating dynamic variables to improve pixel prediction accuracy.The carrier image is subsequently reconstructed using the Huffman coding technique.Encrypted image generation is then achieved by encrypting the image based on system user attributes and data access rights,with the hierarchical embedding of the group’s secret data seamlessly integrated during the encryption process using the CP-ABE scheme.Ultimately,the encrypted image is transmitted to the data hider,enabling independent embedding of the secret data and resulting in the creation of the marked encrypted image.This approach allows only the receiver to extract the authorized group’s secret data,thereby enabling fine-grained,controlled access.Test results indicate that,in contrast to current algorithms,the method introduced here considerably improves the embedding rate while preserving lossless image recovery.Specifically,the average maximum embedding rates for the(3,4)-threshold and(6,6)-threshold schemes reach 5.7853 bits per pixel(bpp)and 7.7781 bpp,respectively,across the BOSSbase,BOW-2,and USD databases.Furthermore,the algorithm facilitates permission-granting and joint-decryption capabilities.Additionally,this paper conducts a comprehensive examination of the algorithm’s robustness using metrics such as image correlation,information entropy,and number of pixel change rate(NPCR),confirming its high level of security.Overall,the algorithm can be applied in a multi-user and multi-level cloud service environment to realize the secure storage of carrier images and secret data.

A Novel Steganography Scheme Combining Coverless Information Hiding and Steganography

At present,the coverless information hiding has been developed.However,due to the limited mapping relationship between secret information and feature selection,it is challenging to further enhance the hiding capacity of coverless information hiding.At the same time,the steganography algorithm based on object detection only hides secret information in foreground objects,which contribute to the steganography capacity is reduced.Since object recognition contains multiple objects and location,secret information can be mapped to object categories,the relationship of location and so on.Therefore,this paper proposes a new steganography algorithm based on object detection and relationship mapping,which integrates coverless information hiding and steganography.In this method,the coverless information hiding is realized by mapping the object type,color and secret information in object detection method.At the same time,the object detection method is used to find the safe area to hide secret messages.The proposed algorithm can not only improve the steganographic capacity of the two information hiding methods but also make the coverless information hiding more secure and robust.

Scheme for Secure Communication via Information Hiding Based on Key Exchange and Decomposition Protocols

This paper considers a decomposition framework as a mechanism for information hiding for secure communication via open network channels. Two varieties of this framework are provided: one is based on Gaussian arithmetic with complex modulus and another on an elliptic curve modular equation. The proposed algorithm is illustrated in a numerical example.

Information Hiding Method Based on Block DWT Sub-Band Feature Encoding

For realizing of long text information hiding and covert communication, a binary watermark sequence was obtained firstly from a text file and encoded by a redundant encoding method. Then, two neighboring blocks were selected at each time from the Hilbert scanning sequence of carrier image blocks, and transformed by 1-level discrete wavelet transformation (DWT). And then the double block based JNDs (just noticeable difference) were calculated with a visual model. According to the different codes of each two watermark bits, the average values of two corresponding detail sub-bands were modified by using one of JNDs to hide information into carrier image. The experimental results show that the hidden information is invisible to human eyes, and the algorithm is robust to some common image processing operations. The conclusion is that the algorithm is effective and practical.

Tufting Carpet Machine Information Model Based on Object Linking and Embedding for Process Control Unified Architecture

In view of the lack of research on the information model of tufting carpet machine in China,an information modeling method based on Object Linking and Embedding for Process Control Unified Architecture(OPC UA)framework was proposed to solve the problem of“information island”caused by the differentiated data interface between heterogeneous equipment and system in tufting carpet machine workshop.This paper established an information model of tufting carpet machine based on analyzing the system architecture,workshop equipment composition and information flow of the workshop,combined with the OPC UA information modeling specification.Subsequently,the OPC UA protocol is used to instantiate and map the information model,and the OPC UA server is developed.Finally,the practicability of tufting carpet machine information model under the OPC UA framework and the feasibility of realizing the information interconnection of heterogeneous devices in the tufting carpet machine digital workshop are verified.On this basis,the cloud and remote access to the underlying device data are realized.The application of this information model and information integration scheme in actual production explores and practices the application of OPC UA technology in the digital workshop of tufting carpet machine.

Image Hiding Algorithm in Discrete Cosine Transform Domain Based on Grey Prediction and Grey Relational Analysis

Traditional information hiding algorithms cannot maintain a good balance of capacity,invisibility and robustness.In this paper,a novel blind colour image information hiding algorithm based on grey prediction and grey relational analysis in the Discrete Cosine Transform(DCT) domain is proposed.First,this algorithm compresses the secret image losslessly based on the improved grey prediction GM(1,1)(IGM) model.It then chooses the blocks of rich texture in the cover image as the embedding regions using Double-dimension Grey Relational Analysis(DGRA).Finally,it adaptively embeds the compressed secret bits stream into the DCT domain mid-frequency coefficients,which are decided by those blocks' Double-Dimension Grey Correlation Degree(DGCD) and Human Visual System(HVS).This method can ensure an adequate balance between invisibility,capacity and robustness.Experimental results show that the proposed algorithm is robust against JPEG compression(46.724 6 dB when the compression quality factor is 90%),Gaussian noise(45.531 3 dB when the parameter is(0,0.000 5)) etc.,and it is a blind information hiding algorithm that can be extracted without an original carrier.

An Information Hiding Algorithm Based on Bitmap Resource of Portable Executable File

An information hiding algorithm is proposed, which hides information by embedding secret data into the palette of bitmap resources of portable executable （PE） files. This algorithm has higher security than some traditional ones because of integrating secret data and bitmap resources together. Through analyzing the principle of bitmap resources parsing in an operating system and the layer of resource data in PE files, a safe and useful solution is presented to solve two problems that bitmap resources are incorrectly analyzed and other resources data are confused in the process of data embedding. The feasibility and effectiveness of the proposed algorithm are confirmed through computer experiments.

Coverless Information Hiding Based on the Molecular Structure Images of Material

The traditional information hiding methods embed the secret information by modifying the carrier,which will inevitably leave traces of modification on the carrier.In this way,it is hard to resist the detection of steganalysis algorithm.To address this problem,the concept of coverless information hiding was proposed.Coverless information hiding can effectively resist steganalysis algorithm,since it uses unmodified natural stego-carriers to represent and convey confidential information.However,the state-of-the-arts method has a low hidden capacity,which makes it less appealing.Because the pixel values of different regions of the molecular structure images of material(MSIM)are usually different,this paper proposes a novel coverless information hiding method based on MSIM,which utilizes the average value of sub-image’s pixels to represent the secret information,according to the mapping between pixel value intervals and secret information.In addition,we employ a pseudo-random label sequence that is used to determine the position of sub-images to improve the security of the method.And the histogram of the Bag of words model(BOW)is used to determine the number of subimages in the image that convey secret information.Moreover,to improve the retrieval efficiency,we built a multi-level inverted index structure.Furthermore,the proposed method can also be used for other natural images.Compared with the state-of-the-arts,experimental results and analysis manifest that our method has better performance in anti-steganalysis,security and capacity.

A novel quantum information hiding protocol based on entanglement swapping of high-level Bell states

Using entanglement swapping of high-level Bell states, we first derive a covert layer between the secret message and the possible output results of the entanglement swapping between any two generalized Bell states, and then propose a novel high-efficiency quantum information hiding protocol based on the covert layer. In the proposed scheme, a covert channel can be built up under the cover of a high-level quantum secure direct communication （QSDC） channel for securely transmitting secret messages without consuming any auxiliary quantum state or any extra communication resource. It is shown that this protocol not only has a high embedding efficiency but also achieves a good imperceptibility as well as a high security.

An Efficient Information Hiding Scheme Based on Closest Paired Tree Structure Vector Quantization

Information hiding schemes based on vector quantization （VQ） usually require lengthy VQ encoding and decoding processes. In this paper, we propose an efficient information hiding method based on closest paired tree structure vector quantization （CPTSVQ）. The simulation result shows that the execution time of the proposed scheme is much shorter than that attained by previous approaches.

Reversible Data Hiding in Encrypted Images Based on Prediction and Adaptive Classification Scrambling

Reversible data hiding in encrypted images(RDH-EI)technology is widely used in cloud storage for image privacy protection.In order to improve the embedding capacity of the RDH-EI algorithm and the security of the encrypted images,we proposed a reversible data hiding algorithm for encrypted images based on prediction and adaptive classification scrambling.First,the prediction error image is obtained by a novel prediction method before encryption.Then,the image pixel values are divided into two categories by the threshold range,which is selected adaptively according to the image content.Multiple high-significant bits of pixels within the threshold range are used for embedding data and pixel values outside the threshold range remain unchanged.The optimal threshold selected adaptively ensures the maximum embedding capacity of the algorithm.Moreover,the security of encrypted images can be improved by the combination of XOR encryption and classification scrambling encryption since the embedded data is independent of the pixel position.Experiment results demonstrate that the proposed method has higher embedding capacity compared with the current state-of-the-art methods for images with different texture complexity.

A Survey of Image Information Hiding Algorithms Based on Deep Learning

With the development of data science and technology,information security has been further concerned.In order to solve privacy problems such as personal privacy being peeped and copyright being infringed,information hiding algorithms has been developed.Image information hiding is to make use of the redundancy of the cover image to hide secret information in it.Ensuring that the stego image cannot be distinguished from the cover image,and sending secret information to receiver through the transmission of the stego image.At present,the model based on deep learning is also widely applied to the field of information hiding.This paper makes an overall conclusion on image information hiding based on deep learning.It is divided into four parts of steganography algorithms,watermarking embedding algorithms,coverless information hiding algorithms and steganalysis algorithms based on deep learning.From these four aspects,the state-of-the-art information hiding technologies based on deep learning are illustrated and analyzed.

On Hiding Secret Information in Medium Frequency DCT Components Using Least Significant Bits Steganography

This work presents a new method of data hiding in digital images,in discrete cosine transform domain.The proposed method uses the least significant bits of the medium frequency components of the cover image for hiding the secret information,while the low and high frequency coefficients are kept unaltered.The unaltered low frequency DCT coefficients preserves the quality of the smooth region of the cover image,while no changes in the high DCT coefficient preserve the quality of the edges.As the medium frequency components have less contribution towards energy and image details,so the modification of these coefficients for data hiding results in high quality stego images.The distortion due to the changes in the medium frequency coefficients is insignificant to be detected by the human visual system.The proposed methods demonstrated a hiding capacity of 43:11%with the stego image quality of a peak signal to the noise ration of 36:3 dB,which is significantly higher than the threshold of 30 dB for a stego image quality.The proposed technique is immune to steganalysis and has proved to be highly secured against both spatial and DCT domain steganalysis techniques.

A Block Mean Insertion and Dual Stego Generation Based Embedding Strategy

In reversible data hiding, pixel value ordering is an up-to-the-minute research idea in the field of data hiding. Secret messages are embedded in the maximum or the minimum value among the pixels in a block. Pixel value ordering helps identify the embeddable pixels in a block but suffers from fewer embedding payloads. It leaves many pixels in a block without implanting any bits there. The proposed scheme in this paper resolved that problem by allowing every pixel to conceive data bits. The method partitioned the image pixels in blocks of size two. In each block, it first orders these two pixels and then measures the average value. The average value is placed in the middle of these two pixels. Thus, the scheme extends the block size from two to three. After applying the embedding method of Weng et al., the implantation task removed the average value from the block to reduce its size again to two. These two alive pixels are called stego pixels, which produced a stego image. A piece of state information is produced during implanting to track whether a change is happening to the block’s cover pixels. This way, after embedding in all blocks, a binary stream of state information is produced, which has later been converted to decimal values. Thus, image data were assembled in a two-dimensional array. Considering the array as another image plane, Weng et al.’s method is again applied to embed further to produce another stego image. Model validation ensured that the proposed method performed better than previous work in this field.

Reversible Data Hiding in Encrypted Images Based on Adaptive Prediction and Labeling

Recently,reversible data hiding in encrypted images(RDHEI)based on pixel prediction has been a hot topic.However,existing schemes still employ a pixel predictor that ignores pixel changes in the diagonal direction during prediction,and the pixel labeling scheme is inflexible.To solve these problems,this paper proposes reversible data hiding in encrypted images based on adaptive prediction and labeling.First,we design an adaptive gradient prediction(AGP),which uses eight adjacent pixels and combines four scanning methods(i.e.,horizontal,vertical,diagonal,and diagonal)for prediction.AGP can adaptively adjust the weight of the linear prediction model according to the weight of the edge attribute of the pixel,which improves the prediction ability of the predictor for complex images.At the same time,we adopt an adaptive huffman coding labeling scheme,which can adaptively generate huffman codes for labeling according to different images,effectively improving the scheme’s embedding performance on the dataset.The experimental results show that the algorithm has a higher embedding rate.The embedding rate on the test image Jetplane is 4.2102 bpp,and the average embedding rate on the image dataset Bossbase is 3.8625 bpp.

Information hiding scheme for vector maps based on fingerprint certification

An information hiding scheme for vector maps is presented to identify the source after the vector map is leaked in some key application areas. In this scheme, the fingerprint image of the map owner can be converted into a character string as the watermark, and then the watermark will be embedded into the coordinate descriptions of the attribute file by the "0-bit value" programming method. This programming algorithm ensures that the accuracy is lossless and the graphics is unchanged for any vector map. Experiments show that the presented hiding scheme has stable robustness, the average similarity rate is 97.2% for fingerprints matching and the false non-match rate is 1.38% in the blocking test. In the opening test, the former reaches 84.46% and the latter reaches 5.56%.

AN OPTIMAL INFORMATION HIDING ALGORITHM FOR SPEECH IN THE FRACTIONAL FOURIER TRANSFORM DOMAIN

With the increasing requirement of military and security, the technology of information hiding for speech becomes a hotspot and difficulty in the fields of speech signal processing and in-formation security, which is developing rapidly. In order to stand against the stegano-analysis, the paper proposed an optimal information hiding algorithm for speech in the Fractional Fourier Transform (FrFT) domain based on the Minimum Mean Square Error (MMSE) criterion. The results of simulation and experiments show that speech modified by the proposed algorithm has no remarkable changes both in time and frequency domains, which can effectively resist the time and frequency analysis, Otherwise, the algorithm is robust to general signal process attack, and the difference is imperceptible between the original and modified speech.

STUDY ON AUDIO INFORMATION HIDING METHOD BASED ON MODIFIED PHASE PARTITION

Hiding efficiency of traditional audio information hiding methods is always low since the sentience similarity cannot be guaranteed. A new audio information hiding method is proposed in this letter which can impose the insensitivity with the audio phase for auditory and realize the information hiding through specific algorithm in order to modify local phase within the auditory perception. The algorithm is to introduce the operation of "set 1" and "set 0" for every phase vectors, then the phases must lie on the boundary of a phase area after modified. If it lies on "1" boundary, it comes by set 1 operation. If it lies on "0" boundary, it comes by set 0 operation. The results show that, compared with the legacy method, the proposed method has better auditory similarity, larger information embedding capacity and lower code error rate. As a kind of blind detect method, it fits for application scenario without channel interference.