The steganography detection method based on deep learning fuse feature extraction and classification into one model, which reduces the human intervention in feature extraction and obtains a higher detection accuracy than the traditional steganography detection method based on manual feature extraction. However, many existing steganography detection methods based on deep learning generally need to increase the depth and width of the model to further improve the detection accuracy, but it also brings the increase of parameters and Flops (FLoating point OPerations) of the model, resulting in a large consumption of computing resources. Therefore, this manuscript proposes a Convolutional Neural Network(CNN) steganography detection method based on ShuffleBottleneck and Attention mechanism (referred to as ShuffleBANet method, ShuffleBottleneck-Attention-Network Based Method). First, to enhance the network's recognition ability for steganography signals, high-pass filters are used to enhance the steganography feature signals and combined with the improved ShuffleBottleneck structure to enrich the residual features and improve the expression ability of features. Then, a large convolution kernel is used to increase the convolution field of view, and the channel attention mechanism SE (Squeeze and Excitation) and the spatial attention mechanism CA(CoordAttention) are used to capture the residual features between channels and the location information of steganography signals, respectively. Combined with the siamese network framework, the feature extraction backbone is constructed. Finally, the features of the backbone are fused to increase the diversity of features, cover features and stego features are classified using the Softmax function. In this manuscript, BossBase-1.01, BOWS2 and ALASKA#2 datasets are used as cover images, the classical and latest steganalysis methods are used to conduct extensive experiments on the stego images generated by both spatial domain and JPEG domain adaptive steganography algorithms. The experimental results show that compared with the latest SiaStegNet method and the classical SRNet method, the detection efficiency of the proposed method is significantly improved, and the number of parameters and Flops has been reduced by 7.04% / 76.23% and 70.92% / 84.49%, respectively, which provides a solution for the lightweight steganography detection model based on deep learning.

The covert communication technology represented by image steganography can realize the safe and reliable information transmission over an open network. Steganalysis is an important way to measure the security of steganography. The application of deep learning in the field of steganalysis has begun to emerge and achieved certain results. However, the existing steganalysis methods based on deep learning extract limited global features and the detection accuracy still needs to be improved. Therefore, this manuscript proposes an improving CoatNet for spatial and JPEG domain steganalysis (IMCoatNet). Firstly, the SKAttention structure is used to extract fine-grained features. Then, the convolution layer, Transformer layer and the pyramid squeezed attention is used to capture multi-scale spatial features. Finally, a weighted loss is designed to enhance the expression ability of the model. The results show that compared with the traditional Spatial Rich Model (SRM) method, the classical deep residual network for steganalysis (SRNet), and the latest siamese CNN for steganalysis (SiaStegNet), the detection accuracy of the proposed CoatNet method for WOW steganography is improved by 19.61%, 4.33% and 5.04%, respectively; The proposed IMCoatNet is also superior on JPEG domain detection.

Abstract The steganalysis for JPEG image is an important research topic, as the enormous popularity of JPEG image on Internet. However, the stego noise feature extraction process of the existing deep learning‐based steganalytic methods are not adaptive enough to the content of the image, which may lead to suboptimal steganalysis performance. In order to solve this issue, an adaptive stego noise extraction network, named SNENet, for JPEG image steganalysis is proposed. The stego noise extraction module of the network is specifically designed for steganalysis, which consists of parallel dilated convolutional layer and inverted bottleneck layer. This specific design expands the receptive field of the network, which makes the extraction of the stego noise more global and adaptive to the content of the image. The experimental results indicate that proposed network outperforms the state‐of‐the‐art steganalytic method by as much as 6.25% for UED‐JC and 3.35% for J‐UNIWARD. The design of the network is also justified in the extensive ablation experiments.

With the rapid proliferation of urbanization, massive data in social networks are collected and aggregated in real time, making it possible for criminals to use images as a cover to spread secret information on the Internet. How to determine whether these images contain secret information is a huge challenge for multimedia computing security. The steganalysis method based on deep learning can effectively judge whether the pictures transmitted on the Internet in urban scenes contain secret information, which is of great significance to safeguarding national and social security. Image steganalysis based on deep learning has powerful learning ability and classification ability, and its detection accuracy of steganography images has surpassed that of traditional steganalysis based on manual feature extraction. In recent years, it has become a hot topic of the information hiding technology. However, the detection accuracy of existing deep learning based steganalysis methods still needs to be improved, especially when detecting arbitrary-size and multi-source images, their detection efficientness is easily affected by cover mismatch. In this manuscript, we propose a steganalysis method based on Inverse Residuals structured Siamese network (abbreviated as SiaIRNet method, Sia mese- I nverted- R esiduals- Net work Based method). The SiaIRNet method uses a siamese convolutional neural network (CNN) to obtain the residual features of subgraphs, including three stages of preprocessing, feature extraction, and classification. Firstly, a preprocessing layer with high-pass filters combined with depth-wise separable convolution is designed to more accurately capture the correlation of residuals between feature channels, which can help capture rich and effective residual features. Then, a feature extraction layer based on the Inverse Residuals structure is proposed, which improves the ability of the model to obtain residual features by expanding channels and reusing features. Finally, a fully connected layer is used to classify the cover image and the stego image features. Utilizing three general datasets, BossBase-1.01, BOWS2, and ALASKA#2, as cover images, a large number of experiments are conducted comparing with the state-of-the-art steganalysis methods. The experimental results show that compared with the classical SID method and the latest SiaStegNet method, the detection accuracy of the proposed method for 15 arbitrary-size images is improved by 15.96% and 5.86% on average, respectively, which verifies the higher detection accuracy and better adaptability of the proposed method to multi-source and arbitrary-size images in urban scenes.

Facing the challenge of small open labeled data sets in remote sensing change detection, this letter proposes a novel supervised change detection method by taking advantages of capsule network which can reach the same performance as traditional convolutional neural networks (CNNs) but with less training data. To achieve this aim, we propose a pseudo-Siamese capsule network which takes both rotational invariance and spatial hierarchies between features into account for aerial images change detection. First, the features of image pairs are extracted by two identical nonshared weights convolutional capsule networks. Second, the extracted features are directly concatenated and sent to another convolutional capsule layer. The change probability map is obtained by calculating the length of the capsule vectors in the final layer. Additionally, to reduce the influence of imbalance samples when we optimize our network, we design a margin-focal loss function to pay more attention to the misclassified samples. Finally, binary change map can be produced by a simple threshold. Experimental results carried out on the SZTAKI AirChange Benchmark Set show that the proposed method achieves comparable and even better results with existing state-of-the-art methods in terms of F-measure.

Lightweight object detector is currently gaining more and more popularity in remote sensing. In general, it’s hard for lightweight detectors to achieve competitive performance compared to traditional deep models, while knowledge distillation is a promising training method to tackle the issue. Since the background is more complicated and the object size varies extremely in remote sensing images, it will deliver lots of noise and affect the training performance when directly applying the existing knowledge distillation methods. To tackle the above problems, we propose an Adaptive Reinforcement Supervision Distillation (ARSD) framework to promote the detection capability of the lightweight model. Firstly, we put forward a multiscale core features imitation (MCFI) module for transferring the knowledge of features, which can adaptively select the multiscale core features of objects for distillation and focus more on the features of small objects by an area-weighted strategy. In addition, a strict supervision regression distillation (SSRD) module is designed to select the optimal regression results for distillation, which facilitates the student to effectively imitate the more precise regression output of the teacher network. Massive experiments on the DOTA, DIOR, and NWPU VHR-10 datasets prove that ARSD outperforms the existing distillation SOTA methods. Moreover, the performance of lightweight model trained with our method transcends other classic heavy and lightweight detectors, which beneficiates the development of lightweight models.

With the global promotion and application of 5G technology, the data transmitted on the network show explosive growth, and the secure sharing of its important data is still one of the research hotspots. Steganography embeds the data that needs to be shared into digital carrier files and transmits it through open channels, which has important applications in protecting data sharing and realizing covert communication. However, the encrypted files generated by traditional steganography are susceptible to compression, cropping, geometric attacks, and man-made destruction in the process of open channel transmission, resulting in data loss, making it difficult for the receiver to correctly extract secret message. Currently, covert communication on blockchain can solve the above problems, but it also brings some new problems such as high computational complexity, low transmission efficiency, and nondetection resistance. Therefore, in this article, a covert communication method based on Bitcoin transactions is proposed. The proposed method first designs the index matrix of the transaction address. Then, the address interaction relationship that carries the secret message through the transaction index matrix is constructed. Finally, the address interaction relationship that carries the secret message is combined with the transaction amount to complete the covert transmission of the secret message on the blockchain environment. The proposed method improves the security and embedding efficiency of covert communication, reduces the number of transactions, and ensures the integrity of extracting secret message. A series of experimental results show that under the condition of ensuring the necessary security, the proposed method retains the strong robustness of the existing blockchain steganography and has strong resistance to detection.

Motivated by the development of deep convolution neural networks (DCNNs), aircraft detection has gained tremendous progress. State-of-the-art DCNN-based detectors mainly belong to top-down approaches, which enumerate massive potential locations of aircraft with the form of rectangular regions, and then identify whether they are objects or not. Compared with these top-down detectors, this article shows that aircraft detection via a type of bottom-up method can have better performances in the era of deep learning. In this article, we propose a novel bottom-up detector named X-LineNet. It formulates the aircraft detection task as prediction and clustering of paired intersecting line segments inside each target. Aircraft detection is then a purely appearance-based line segments estimation problem, without any rectangular regions classification or implicit features learning. With simple postprocessing, X-LineNet can simultaneously provide multiple representation forms of the detection result: the horizontal bounding box, the oriented bounding box, and the pentagonal mask. The pentagonal mask is a more accurate representation form of aircraft which has less redundancy than that of a rectangular box. Experiments show that X-LineNet outperforms prevalent top-down and region-based detectors on UCAS-AOD, NWPU VHR-10, and DIOR public data sets in the field of aircraft detection.

The performance of object instance segmentation in remote sensing images has been greatly improved through the introduction of many landmark frameworks based on convolutional neural network. However, the object densely issue still affects the accuracy of such segmentation frameworks. Objects of the same class are easily confused, which is most likely due to the close docking between objects. We think context information is critical to address this issue. So, we propose a novel framework called SLCMASK-Net, in which a sequence local context module (SLC) is introduced to avoid confusion between objects of the same class. The SLC module applies a sequence of dilation convolution blocks to progressively learn multi-scale context information in the mask branch. Besides, we try to add SLC module to different locations in our framework and experiment with the effect of different parameter settings. Comparative experiments are conducted on remote sensing images acquired by QuickBird with a resolution of 0.5m - 1m and the results show that the proposed method achieves state-of-the-art performance.

In this paper, we present a semi-supervised remote sensing change detection method based on graph model with Generative Adversarial Networks (GANs). Firstly, the multi-temporal remote sensing change detection problem is converted as a problem of semi-supervised learning on graph where a majority of unlabeled nodes and a few labeled nodes are contained. Then, GANs are adopted to generate samples in a competitive manner and help improve the classification accuracy. Finally, a binary change map is produced by classifying the unlabeled nodes to a certain class with the help of both the labeled nodes and the unlabeled nodes on graph. Experimental results carried on several very high resolution remote sensing image data sets demonstrate the effectiveness of our method.