False data injection attacks (FDIAs) significantly disrupt the secure operation of smart grids by manipulating the measured values collected by intelligent instruments. Existing studies have utilized deep learning techniques to enhance the detection of FDIAs, however, these studies often overlook the spatial correlation between power grid topology and measurement data. Meanwhile, the high complexity of deep neural network severely impedes the interpretability of detection models, resulting in the incredibility of detection results. To address the above challenges, this paper proposes an interpretable deep learning FDIAs detection method, named dual-attention multi-head graph attention network, DAMGAT. The DAMGAT introduces a dual-attention mechanism that incorporates both node feature attention and spatial topology attention into a multi-head graph attention network. This mechanism efficiently aggregates attack characteristics and spatial topology information by dynamically capturing the potential correlations between FDIAs detection and measurement data. Furthermore, the proposed model can provide clear and credible interpretations for high-accuracy detection results via analyzing features and spatial topology attention weights. Extensive simulations are performed using the IEEE 14-bus and 118-bus test systems. The experimental results demonstrate that the proposed model outperforms state-of-the-art FDIA detection methods in terms of accuracy, while also providing reasonable interpretability for features and spatial dimensions.

JPEG image encryption aims at effectively converting the original JPEG image into a noise-like image that does not contain any useful information of original image. Existing schemes for JPEG image encryption, however, may not attain a good balance in terms of file size increment and encryption security. To address the problem, we design a novel JPEG image encryption scheme. Different from existing schemes, we first predict DC coefficients by an adaptive prediction method. Subsequently, the histogram of DC coefficient prediction errors is encrypted by combining the prediction errors and random integers to reduce the encoded length, which can ensure a very small increment of file size. Furthermore, we construct the RS (run/size) pairs in each DCT block and then implement the permutation for both RS pairs extracted from the upper left corner of each DCT block and all DCT blocks excluding DC coefficients, which can further distort the image contents. Extensive experiments demonstrate that, compared with existing JPEG image encryption schemes, our scheme can ensure not only the JPEG format compatibility for encrypted image, but also keep a very small file size increment and the superior security performance.

Multi-image steganography refers to a steganographic method where a user tries to hide multiple confidential images within a single cover image, and all confidential images can be correspondingly recovered perfectly by the recipient. Multi-image steganography essentially belongs to a high-capacity image steganographic scheme, but such high hiding capacity may easily cause severe contour shadows or color distortion of steganographic images, resulting in a significant reduction in anti-steganalysis capability. To address the above problem, this paper designs a deep invertible neural network by introducing spatial-channel joint attention mechanism, in which the confidential image hiding and recovery can be regarded as a pair of coupled invertible processes. Specifically, a series of simple invertible networks having the same structure are firstly used to construct a cascaded deep invertible neural network framework, in which multiple confidential images can be sequentially embedded into a single cover image through a series of flexible cascaded iterative operations. Subsequently, spatial-channel joint attention module is designed to re-construct invertible network model, which can guide the embedding of secret information into more secure image regions. Accordingly, this joint attention mechanism can effectively address the problem of visual quality and security degradation of steganographic images due to high embedding capacity. Extensive experiments demonstrate that our scheme can obtain superior performance over different large-scale image sets, and outperforms state-of-the art methods with higher visual quality and stronger anti-steganalysis capability.

In this paper, an effective print-camera (P-C) resistant image watermarking scheme is proposed. To achieve watermark robustness, most of existing works try to simulate P-C noise by a sophisticated math model. However, the diversity of P-C noises in the real world is ignored, and the watermarked image may not attain a good balance between high robustness and low distortion. To address the problem, we construct an efficient end-to-end network architecture for watermark embedding and extraction. To be specific, a deep noise simulation network (NSN) is designed to simulate the fusion process of real P-C noises, which can help to generate high-robust watermarked image. Also, a multitask loss function based on just-noticeable-difference (JND) is proposed to conduct constrained learning for residual image containing watermark information, thus, the distortion of generated watermarked image can be significantly reduced. Experimental results show that our scheme can achieve high robustness against P-C process while maintaining a satisfactory watermark capacity and visual quality of watermarked image.

Image manipulation localization is a technique that can efficiently segment the tampered regions from a suspicious image. Existing work usually trains a detection model by fusing the features from diverse data streams, e.g., noise inconsistency, recompression inconsistency, and local inconsistency. They, however, ignore a fact that not all tampered images contain these data streams. As a result, high feature redundancy may cause a large number of false detection for tampered region. To address this problem, this paper designs an end-to-end high-confidence localization network architecture. First, deep convolutional neural networks are utilized to extract multi-scale feature sets from the RGB streams. We then design a semantic refined bi-directional feature integration module to fully fuse multi-scale adjacent features and significantly enhance feature representation. Subsequently, morphological operations are introduced to extract multi-scale edge information, which can efficiently reduce feature redundancy by generating wider high-resolution edges during image reconstructing. Finally, a deep semantic residual decoder is sequentially re-constructed by spreading deep semantic information into each decoding stage. The proposed method can not only improve the manipulation localization accuracy, but also guarantee the model robustness. Extensive experiments demonstrate that our method can obtain an effective performance in locating forged regions over different large-scale image sets, and outperforms most of state-of-the-art methods with higher localization accuracy and stronger robustness.

With the development of social network, a large number of private JPEG images are stored in social cloud platform. Correspondingly, the platform embeds user ID or authentication labels to manage these privacy images, preventing them from being arbitrarily accessed or tampered by illegal persons. However, data embedding in JPEG domain inevitably produces irreversible modifications to DCT coefficients, thus resulting in obvious or even serious distortion in the host JPEG images. To address this problem, this paper proposes an efficient JPEG reversible data hiding (RDH) method by constructing progressive two-dimensional histogram mappings. We firstly design distortion function to calculate the cost of each DCT frequency band, and then sort them to build histogram mapping containing a series of coefficient pairs. Subsequently, a progressive mapping mechanism is introduced to maintain most of AC coefficients unchanged. According to the given capacity, this mechanism can adaptively generate an optimum two-dimensional histogram mapping to embed secret messages. Our scheme can achieve an effective balance among embedding capacity, visual quality of the marked image and file size expansion, while keeping high cost-performance complexity. Extensive experiments demonstrate that our method outperforms existing JPEG RDH schemes in terms of visual quality and file size increment of the marked image, and provides an efficient solution for the confidentiality and security access problem of sensitive private image in cloud environment.