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Abstract Digital image steganography serves as a technology facilitating covert communication through digital images by subtly incorporating secret data into a cover image. This practice poses a potential threat, as criminals exploit steganography to transmit illicit content, thereby jeopardizing information security. Consequently, it becomes imperative to implement defensive strategies against steganographic techniques. This paper proposes a novel defense mechanism termed “image vaccine” to safeguard digital images from steganography. The process of “vaccinating” an image renders it immune to steganographic manipulation. Notably, when criminals attempt to embed secret data into vaccinated images, the presence of such hidden information can be detected with a 100% probability, ensuring the consistent identification of stego images. This proactive approach enables the interception of stego image transmission, thereby neutralizing covert communication channels.

The advancement of internet has greatly facilitated development of healthcare technology, particularly in the field of remote diagnosis. However, during the transmission of medical images and electronic medical records through internet, there are risks such as theft, tampering, and compression, which may result in leakage or damage of patients’ information. A robust zero-watermarking scheme using DT CWT and improved differential entropy is proposed to protect the copyright and security of medical images. Firstly, the low-frequency information of medical image is extracted by DT CWT to weaken the impact of non-geometric attacks. Then, a sub-block selection method based on variance is used to select locally stable sub-blocks, and the traditional differential entropy method is improved by introducing singular values and their mean, which improves its robustness against geometric attacks. In order to ensure the security of zero-watermarking, Arnold scrambling and Logistic mapping encryption mechanisms are applied to watermark images. Experimental results show that the proposed scheme provides a high level of robustness with an average normalized correlation value of 0.9928. Compared with other advanced schemes, the robustness of the proposed scheme can be improved by 9 percentage points under the same attacks, which verifies the superiority of the proposed scheme.

The images with non-fungible token (NFT) are employed as the digital artistic works in metaverse for creation, transaction, sharing, and collection.Being different from natural images, the content of NFT images is defined by user and distributed in the digital space widely.It is convenient for the hidden of secret data.In this case, covert communication with NFT images is a new branch of image steganography.Then, a steganographic method for NFT images was proposed accordingly.Given a NFT image, the regions of its profile and the components with high frequency were enhanced firstly to enrich the details which were beneficial to hide the modification trace of steganography.In this way, the enhanced image was used as cover since it is more suitable for steganography.Then, the tendency modification direction of each pixel was determined by the differences between the enhanced image and the given image.The differences were also used to determine the cost value of modification amplitude.Thus, the undetectability of steganography can be increased further.Secret data was embedded into the cover image using the popular steganographic coding schemes.Experimental results showed that the proposed method had imporoved undetectability on NFT images compared with existing digital steganographic schemes.Compared with HILL, MiPOD, and DEFI, the proposed method can increase the detection error PE of steganalysis by 8.7%, 9.2% and 6.2%, respectively (the average value for the cases of different payload and steganalytic features).Therefore, the proposed method is suitable for NFT images and it provides targeted steganographic method for the third kind of images, i.e., NFT images, except of natural images and generated images.For further study, the deep learning-based steganographic method can be designed for NFT images using the strong fitting and learning ability of neural networks.

Natural language generation (NLG) models combined with increasingly mature and powerful deep learning techniques have been widely used in recent years. Deployed NLG models in practical applications may be stolen or used illegally, and watermarking has become an important tool to protect the Intellectual Property (IP) of these deep models. Watermarking technique designs algorithms to embed watermark information and extracts watermark information for IP identification of NLG models can be seen as a symmetric signal processing problem. In terms of IP protection of NLG models, however, the existing watermarking approaches cannot provide reliable and timely model protection and prevent illegal users from utilizing the original performance of the stolen models. In addition, the quality of watermarked text sequences generated by some watermarking approaches is not high. In view of these, this paper proposes two embedding schemes to the hidden memory state of the RNN to protect the IP of NLG models for different tasks. Besides, we add a language model loss to the model decoder to improve the grammatical correctness of the output text sequences. During the experiments, it is proved that our approach does not compromise the performance of the original NLG models on the corresponding datasets and outputs high-quality text sequences, while forged secret keys will generate unusable NLG models, thus defeating the purpose of model infringement. Besides, we also conduct sufficient experiments to prove that the proposed model has strong robustness under different attacks.

Deep neural networks have achieved remarkable success in various fields of artificial intelligence. However, these models, which contain a large number of parameters, are widely distributed and disseminated by researchers, engineers, and even unauthorized users. Except for intelligent tasks, typically overparameterized deep neural networks have become new digital covers for data hiding, which may pose significant security challenges to AI systems. To address this issue, this paper proposes a symmetric steganalysis scheme specifically designed for neural networks trained for image classification tasks. The proposed method focuses on detecting the presence of additional data without access to the internal structure or parameters of the host network. It employs a well-designed method based on histogram distribution to find the optimal decision threshold, with a symmetric determining rule where the original networks and stego networks undergo two highly symmetrical flows to generate the classification labels; the method has been shown to be practical and effective. SVM and ensemble classifiers were chosen as the binary classifier for their applicability to feature vectors output from neural networks based on different datasets and network structures. This scheme is the first of its kind, focusing on steganalysis for neural networks based on the distribution of network output, compared to conventional digital media such as images, audio, and video. Overall, the proposed scheme offers a promising approach to enhancing the security of deep neural networks against data hiding attacks.

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.

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.

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.

In this paper, we propose a new data-hiding framework: multisource data hiding, in which multiple senders (multiple sources) are able to transmit different secret data to a receiver via the same cover image symmetrically. We propose two multisource data-hiding schemes, i.e., separable and anonymous, according to different applications. In the separable scheme, the receiver can extract the secret data transmitted by all senders using the symmetrical data-hiding key. A sender is unable to know the content of the secret data that is not transmitted by them (non-source sender). In the anonymous scheme, it is unnecessary to extract all secret data on the receiver side. The content extracted by the receiver is a co-determined result of the secret data transmitted by all senders. Details of the secret data are unknown to the receiver and the non-source senders. In addition, the two proposed schemes achieve multisource data hiding without decreasing the undetectability of data hiding.

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.

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. [ABSTRACT FROM AUTHOR]