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This paper proposes a repeatable data hiding framework for digital images, in which the distortion caused by data hiding is invariable no matter how many times the embedding operation is executed. As a result, the usability of images can be always guaranteed. To achieve repeatable data hiding, we deduce the theoretical modification probabilities of the elements in cover image (an image used for data hiding). Then we design an embedding framework to make the practical modification probabilities of cover elements equal to the deduced ones. When additional data is embedded into cover image with the deduced probabilities, the modification-trace of current embedding is replaced by the latter embedding. That means multiple embeddings do not produce new modification-trace, so that the distortion caused by data hiding is invariable. Therefore, the repeatability of data hiding can be guaranteed. Finally, we describe some applications of repeatable data hiding, e.g., logistics management, authentication database management, and steganography, to show the practicability of our framework.

Batch steganography is a technique that hides information into multiple covers. To achieve a better performance on the security of data hiding, we propose a novel strategy of batch steganography using a generative network. In this method, the approaches of cover selection, payload allocation, and distortion evaluation are considered in the round. We define a quality metric to evaluate the distortion between the cover image and the stego. When training the generation function, we define an objective function containing two parts: the entropy loss and the steganalytic loss. While the entropy loss is used to represent the gap between the payload inside stego images and the entire embedding capacity, the steganalytic loss is used to assess the data embedding impact using the proposed quality metric. With back-propagation, we minimize the objective function to obtain an optimal solution. Accordingly, different payloads can be allocated to different images, and the ± 1 modification probability for pixels in each cover can be calculated. Finally, we embed information into the selected images by STC. Experimental results show that the proposed method achieves a better undetectability against modern steganalytic tools.

In this article, we focus on a new concept called multichannel steganography, in which a sender is able to transmit different secret data to multiple receivers via the same cover image. First, we propose a general framework to achieve multichannel steganography based on modern steganographic paradigm. Then, we design two schemes to, respectively, achieve separable and sequential multichannel steganography with the proposed framework. In the separable scheme, a receiver can extract secret data for him/her using the corresponding data hiding key. For other parts of secret data, the receiver cannot affirm the existence and, therefore, cannot extract them. In the sequential scheme, the multiple receivers are dependent. The receiver with the highest priority can extract data first. After that, the receiver with the second priority can extract data when authorized by the highest priority receiver, and so on. In addition, the two schemes achieve multichannel steganography without decreasing the undetectability of steganography.

This paper proposes a new steganography method for hiding data into dynamic GIF (Graphics Interchange Format) images. When using the STC framework, we propose a new algorithm of cost assignment according to the characteristics of dynamic GIF images, including the image palette and the correlation of interframes. We also propose a payload allocation algorithm for different frames. First, we reorder the palette of GIF images to reduce the modifications on pixel values when modifying the index values. As the different modifications on index values would result in different impacts on pixel values, we assign the elements with less impact on pixel values with small embedding costs. Meanwhile, small embedding costs are also assigned for the elements in the regions that the interframe changes are large enough. Finally, we calculate an appropriate payload for each frame using the embedding probability obtained from the proposed distortion function. Experimental results show that the proposed method has a better security performance than state-of-the-art works.

This paper proposes a secure steganographic method in social networks based on the behavioral correlation between the sender and his friends. A sender transmits secret data to one of his friends in...

This letter focus on a practical scenario of cover selection for steganography, in which a part of the available images of the sender have been processed to improve visual quality, e.g., contrast enhancement, image denoising. In this case, not only the embedding distortion (caused by steganography), but also the processing distortion (caused by processing) should be considered when selecting cover image. We propose a cover selection method to combine the two kinds of distortion together to measure the suitability for steganography of the available images. To calculate the processing distortion, a classifier is trained to distinguish the processed images with the original ones. The classifier is then used to measure the possibility of the existence of processing. High possibility means high processing distortion. In addition, the current distortion minimization framework designed for steganography is employed to calculate the embedding distortion. Finally, both kinds of distortion are combined to form the total distortion, and the image with the minimal total distortion is selected as cover. Using the selected image for embedding, high undetectability can be achieved.

Posting images on social network platforms is happening everywhere and every single second. Thus, the communication channels offered by various social networks have a great potential for covert communication. However, images transmitted through such channels will usually be JPEG compressed, which fails most of the existing steganographic schemes. In this paper, we propose a novel image steganography framework that is robust for such channels. In particular, we first obtain the channel compressed version (i.e., the channel output) of the original image. Secret data is embedded into the channel compressed original image by using any of the existing JPEG steganographic schemes, which produces the stego-image after the channel transmission. To generate the corresponding image before the channel transmission (termed the intermediate image), we propose a coefficient adjustment scheme to slightly modify the original image based on the stego-image. The adjustment is done such that the channel compressed version of the intermediate image is exactly the same as the stego-image. Therefore, after the channel transmission, secret data can be extracted from the stego-image with 100% accuracy. Various experiments are conducted to show the effectiveness of the proposed framework for image steganography robust to JPEG compression.

This article describes how to overcome the shortage of steganalysis for small capacity-based embedding. A slight modification method is proposed to break steganography. For a given image, traditional steganalysis methods are first used to achieve a preliminary result. For the “clear” image judged by steganalysis, it is still suspicious because of the incompleteness of steganalysis for small capacity. Thus, slight modifications are made to break the possibility of covert communication. The modifications are made on the locations with minimal distortion to guarantee high quality of the modified image. To this end, a proposed distortion minimization based algorithm using slight modification. Experimental results show that the error rate of secret data extraction is around 50% after implementation, which indicates that the covert communication of steganography is destroyed completely.

This article describes how the existing distortion functions for JPEG steganography allot same cost for ±1 embedding changes. Because of the correlation of natural image, however, changes with different polarities make different influences on image. Therefore, the embedding costs for ±1 embedding changes should not be equivalent. This article proposes a general method to distinguish the embedding costs for different polarities of embedding changes for JPEG images with the help of reference images constructed by block artifact compensation. The original JPEG image is decompressed into spatial domain firstly, and then the block artifact is compensated by smoothing filtering implemented on border pixels of each 8 × 8 block. After that, the compensated image which is more similar to the original uncompressed image is recompressed into DCT domain and adopted as side information to guide the adjusting of the given distortion function. Experiment results show that after the proposed method is employed, the security performance of current popular JPEG steganographic methods is observably increased.

Existing cover selection methods for steganography cannot resist pooled steganalysis. This paper proposes a secure cover selection method which is able to resist pooled steganalysis and single object steganalysis meanwhile. To resist pooled steganalysis, the maximum mean discrepancy (MMD) distance between the stego set and a clear arbitrary image set is kept not larger than a normal threshold during cover selection, where the threshold is the MMD distance between two clear arbitrary image sets. Under this constraint, a searching strategy is designed to select the minimal steganographic distortion images within the affordable computational complexity to resist single object steganalysis. With the selected covers, the security of steganography is guaranteed against both single object steganalysis and pooled steganalysis. The experimental results demonstrated the effectiveness of the proposed method.

Existing distortion functions in steganography which achieved high undetectability are designed for unprocessed natural image. Nowadays, a large number of images are filtered before transmitting for the sake of beautification. In this situation, existing distortion functions should be improved to fit the properties of these beautified images. This paper proposes a distortion function optimization method for steganography on beautified images. Given an unprocessed image, a popular image beautification software is employed to produce two similar beautified images. One of them is used for embedding and the other one is employed as reference. Guided by the reference, existing distortion functions are improved by distinguishing the embedding costs for ±1 embedding. After embedding, the stego image is closer to the reference, which results in a higher undetectability against steganalysis. Experimental results also proved the increasing of undetectability when examined by modern steganalytic tools.

Contrast enhancement is widely used to improve the visual quality of images. This paper proposes a distortion function for steganography in enhanced images. The pixel prediction error and the cost of the pixel value itself are joint to fit the unique properties of enhanced image. Given a cover image, each pixel is predicted by its neighbors. The prediction error, which reflects the texture complexity, are combined with the cost of the pixel value itself to form the final distortion function. With the proposed distortion function, secret data can be embedded into the enhanced cover image with minimal image distortion using syndrome trellis coding (STC). As a result, less detectable artifacts left in the stego images, so that high undetectability is achieved.

This letter proposes a batch steganographic method, which combines cover-selection and payload-allocation by steganographic distortion optimization. We first proved that with the value of payload increasing, the first-order derivative of steganographic distortion of a single cover is monotonically increasing. Then, we deduced that the first-order derivative of steganographic distortion of covers that selected from a given set should be equal if the total steganographic distortion of the corresponding selected covers is minimal. Finally, an algorithm was designed to combine cover-selection and payload-allocation, so that the optimal stego-covers can be obtained. Experiment results show that the undetectability is obviously improved when using the proposed steganographic method.

This paper proposes a framework to improve the existing distortion functions designed for JPEG steganography, which results in a better capability of countering steganalysis. Different from the existing steganography approach that minimizes image distortion, we minimize the feature distortion caused by data embedding. Given a JPEG image, we construct a reference image close to the image before JPEG compression. Guided by both the reference image and the feature distortion minimization, the state-of-the-art distortion functions designed for syndrome trellis coding embedding are improved by distinguishing the embedding costs for +1 versus -1 embedding. This paper has three contributions. First, the proposed framework outperforms the traditional, since we use the constructed reference image and the public steganalytic knowledge for data embedding. Second, the proposed framework is universal for improving distortion functions that were designed for JPEG steganography. Finally, experimental results also prove that the proposed approach has a better undetectability when examined by modern steganalytic tools.

Steganography aims to embed additional data into digital media secretly and minimize the distortion caused by data embedding. Up to now, the key point of minimal distortion embedding framework is to design proper distortion function. In this paper, a distortion function for JPEG steganography based on residual blocks is proposed. To obtain less statistical detectability, Residual Block Values (termed as RBVs for short) and quantization steps are both involved in the proposed distortion function. RBVs are exploited to determine the embedding risk caused by modifications in corresponding DCT block while quantization steps are exploited to determine the embedding risk of the selection channel in a block. By implementing the syndrome trellis coding (STC) to embed secret data, the modifications are constrained in hard-to-detect religions. Experiments show that the proposed method performs better than current state-of-the-art methods of JPEG steganography.

This paper proposes a novel distortion function for JPEG steganography, which depends on the magnitude of discrete cosine transformation (DCT) coefficients, the first- and second-order residuals, a...

This paper proposes an improved steganalytic method when cover selection is used in steganography. We observed that the covers selected by existing cover selection methods normally have different characteristics from normal ones, and propose a steganalytic method to capture such differences. As a result, the detection accuracy of steganalysis is increased. In our method, we consider a number of images collected from one or more target (suspected but not known) users, and use an unsupervised learning algorithm such as $k$ -means to adapt the performance of a pre-trained classifier towards the cover selection operation of the target user(s). The adaptation is done via pseudo-labels from the suspected images themselves, thus allowing the re-trained classifier more aligned with the cover selection operation of the target user(s). We give experimental results to show that our method can indeed help increase the detection accuracy, especially when the percentage of stego images is between 0.3 and 0.7.

This paper proposes a new feature extraction method for steganalysis. In the improved method, the feature set is equipped with two parts. The first part is obtained by employing an existing feature extraction method on a suspicious image. With a content-correlative operation, differences of the image features are enlarge between the cover and the stego. The second part is obtained by employing the same feature extraction method on the obtained image. The two parts are combined together to form the new feature set. With the new feature set, the modifications made by steganography can be recognized more efficiently. Experimental results show that the detection accuracy of existing feature extraction methods is increased using the proposed method.

This paper considers a more practical situation for pooled steganalysis that only a single actor is observed, so that the steganalyst needed to analyze the actor independently without comparing with other actors. We propose a pooled steganalysis method for this situation. For a given actor that has emitted a number of images, feature sets are extracted from each image, respectively, and then feed to a binary classifier popularly used in single object steganalysis. Combining all the results output by the classifier, a final decision is made ensemble to label the given actor as “guilty” or “innocent” with the minimal detection error. Experimental results show that the proposed method is effective for single actor pooled steganalysis.

With the rapid development of deep learning, a lot of CNN-based steganalyzers have emerged. This kind of steganalyzer uses statistical learning to investigate the properties caused by steganography, which is the most efficient approaches for breaking information hiding. However, we find a vulnerability of CNN-based steganalyzer that it can be defeated by dual operations. In this paper, we propose an easy yet effective algorithm to perturb the stego images against neural network, which can evade CNN-based steganalyzer with high probabilities. We elaborated on the theoretical basis of the method we proposed and proved the feasibility of this method through experiments.

International audience; Nowadays, emoji image is widely used in social networks. To achieve covert communication in emoji images, this paper proposes a distortion function for emoji images steganography. The profile of image content, the intra-and inter-frame correlation are taken into account in the proposed distortion function to fit the unique properties of emoji image. The three parts are combined together to measure the risks of detection due to the modification on the cover data. With the popular syndrome trellis coding (STC), the distortion of stego emoji image is minimized using the proposed distortion function. As a result, less detectable artifacts could be found in the stego images. Experimental results show that the proposed distortion function performs much higher undetectability than current state-of-the-art distortion function HILL which is designed for natural image.

This paper proposes a distortion function for steganography in texture synthesized images. Given a small piece of texture, an image synthesis algorithm is employed to generate a texture image in arbitrary size with similar local appearance. The obtained texture image is used as cover for data embedding. A distortion function is designed for the cover image to measure the detection risk of modifications. The image texture, splicing of patches, and repetition of texture blocks are contained in the proposed distortion function to fit the properties of synthesized images, which results in high undetectability against steganalysis. Experimental results also prove that the proposed distortion function performs better than current state-of-the-art steganographic methods.

This paper presents a new general framework of information hiding, in which the hidden information is embedded into a collection of activities conducted by selected human and computer entities (e.g., a number of online accounts of one or more online social networks) in a selected digital world. Different from other traditional schemes, where the hidden information is embedded into one or more selected or generated cover objects, in the new framework the hidden information is embedded in the fact that some particular digital activities with some particular attributes took place in some particular ways in the receiver-observable digital world. In the new framework the concept of "cover" almost disappears, or one can say that now the whole digital world selected becomes the cover. The new framework can find applications in both security (e.g., steganography) and non-security domains (e.g., gaming). For security applications we expect that the new framework calls for completely new steganalysis techniques, which are likely more complicated, less effective and less efficient than existing ones due to the need to monitor and analyze the whole digital world constantly and in real time. A proof-of-concept system was developed as a mobile app based on Twitter activities to demonstrate the information hiding framework works. We are developing a more hybrid system involving several online social networks., 11 pages, 4 figures, accepted to MPS 2018 (2nd International Workshop on Multimedia Privacy and Security)

Most of the existing distortion functions for digital images steganography allot a same embedding cost for ±1 embedding change, which should be different intuitively. This paper proposes a general method to distinguish the embedding cost for different polarity of embedding change for spatial images. The fluctuation after pixels are +1 or −1 modified respectively, and the texture of cover image are employed to adjust a given distortion function. After steganography with the adjusted distortion function, the fluctuation around stego pixels become more similar to the fluctuation around their neighbourhoods. This similarity performs less detectable artifacts. Experiment results show that the statistical undetectability of current popular steganographic methods is increased after incorporated the proposed method.

We propose a steganographic scheme for stylized images. Given a natural image, an image style transfer algorithm is executed twice to produce two similar stylized images with different parameters. One of them is used for embedding and another one is employed as a reference. For the cover image, embedding costs are assigned to measure the detection risk of the modifications. Each embedding cost is then asymmetrically adjusted into two polarity costs to further measure the detection risk of different modification polarities with the guidance of the reference image. After embedding with syndrome-trellis coding, the difference of the stegoimage and the reference image is minimized, which results in a high undetectability against steganalysis. Experimental results also prove that the proposed scheme performs much better than the existing steganographic methods when examined by modern steganalytic tools.

A hybrid distortion function for JPEG steganography exploiting block fluctuation and quantization steps is proposed. To resist multidomain steganalysis, both spatial domain and discrete cosine transformation (DCT) domain are involved in the proposed distortion function. In spatial domain, a distortion value is allotted for each 8×8 block according to block fluctuation. In DCT domain, quantization steps are employed to allot distortion values for DCT coefficients in a block. The two elements, block distortion and quantization steps, are combined together to measure the embedding risk. By employing the syndrome trellis coding to embed secret data, the embedding changes are constrained in complex regions, where modifications are hard to be detected. When compared to current state-of-the-art steganographic methods for JPEG images, the proposed method presents less detectable artifacts.