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A high capacity message embedding in encrypted HEVC video is proposed in this paper. The challenges addressed in this paper include keeping the encrypted video compliant with standardized decoders, correctly decrypting the video and finally, correctly extracting the message bits. The message embedding is achieved by altering the values of reference picture indices and motion vectors which results in scrambled video. Sixteen picture references are used in this work and therefore, combined with alteration of motion vectors, a maximum of six message bits can be embedded per coding unit. Motion vectors are altered by swapping their $x$ and $y$ components and/or changing their signs. This is achieved with full compliance with the HEVC video syntax. To extract message bits, an authorized decoder builds a classification model per video sequence and uses it for predicting the true values of the reference indices and motion vectors. As such, message bits are extracted and the video is correctly reconstructed to its unscrambled state. Coding units that result in misclassification are identified at the encoder and excluded from message embedding. This results in slightly lower embedding rates but ensures accurate video reconstruction. Using nine video sequences of various resolutions that are compressed using four different quantization parameters, the experimental results revealed that the true average message embedding rate is 2.7 bits per coding unit or 173 kbit/s. This is achieved with accurate video reconstruction at the expense of increasing the bitrate of the encoder by 3%. Comparison with existing work shows that the proposed solution is superior in terms of embedding capacity whilst reducing the excessive bitrate of the encoder.

Evaluating group separability is fundamental to pattern recognition. A plethora of dimension reduction (DR) algorithms has been developed to reveal the emergence of geometrical patterns in a low-dimensional space, where high-dimensional sample similarities are approximated by geometrical distances. However, statistical measures to evaluate the group separability attained by DR representations are missing. Traditional cluster validity indices (CVIs) might be applied in this context, but they present multiple limitations because they are not specifically tailored for DR. Here, we introduce a new rationale called projection separability (PS), which provides a methodology expressly designed to assess the group separability of data samples in a DR geometrical space. Using this rationale, we implemented a new class of indices named projection separability indices (PSIs) based on four statistical measures: Mann-Whitney U-test p-value, Area Under the ROC-Curve, Area Under the Precision-Recall Curve, and Matthews Correlation Coefficient. The PSIs were compared to six representative cluster validity indices and one geometrical separability index using seven nonlinear datasets and six different DR algorithms. The results provide evidence that the implemented statistical-based measures designed on the basis of the PS rationale are more accurate than the other indices and can be adopted not only for evaluating and comparing group separability of DR results but also for fine-tuning DR algorithms’ hyperparameters. Finally, we introduce a second methodological innovation termed trustworthiness, a statistical evaluation that accounts for separability uncertainty and associates to the measure of each index a p-value that expresses the significance level in comparison to a null model.

Content-adaptive steganography intends to hide data in the complex texture content of the image. Recently, some secure steganography methods have been proposed to identify the textural complexity of an image. However, most of the techniques do not take into account the information of pixel variation around the central pixel in all possible directions and therefore they are unable to accurately analyse the texture complexity. This work offers a quality-enhanced and secure method of content-adaptive image steganography. The proposed method is divided into three sequential steps: image segmentation, pixel complexity identification, and data embedding. An input cover image is initially divided into small local regions and the pixel-complexity is identified based on the proposed Complex Block Prior (CBP) criterion. In a local block, a high pass filter (HPF) bank is applied and eight residual responses are obtained. Following the CBP criterion, a complexity level out of nine levels is assigned to an individualized pixel block. The pixels are then arranged in the priority of complexity from highest to lowest. Data embedding for the corresponding complexity level then takes place using the proposed adaptive embedding algorithm. Experimental results verify the preservation of visual quality of stego images produced by the proposed method. Three image datasets: Standard test images, BOWS2 and BOSS-base are used for the experimentation and comparison with prior state-of-art methods. Highest values of the IQ (image quality) parameters e.g., SSIM and WPSNR show the effectiveness of the proposed method.

This paper presents an image Steganography algorithm that can work for cover images of multiple formats. Having a single algorithm for multiple image types provides several advantages. For example, we can apply uniform security policies across all image formats, we can adaptively select the most suitable cover image based on data length, network bandwidth and allowable distortions, etc. We present our algorithm based on the abstract concept of image components that can be adapted for JPEG, Bitmap, TIFF and PNG cover images. To the best of our knowledge, the proposed algorithm is the first Steganography algorithm that can work for multiple cover image formats. In addition, we have utilized concepts like capacity pre-estimation, adaptive partition schemes and data spreading to embed secret data with enhanced security. The proposed method is tested for robustness against Steganalysis with favorable results. Moreover, comparative results for the proposed algorithm are very promising for three different cover image formats.

The current existing data in online music service platforms are heterogeneous, extensive, and disorganized. Finding an effective method to use these data in recommending appropriate music to users during a short-term session is a significant challenge. Another serious problem is that most of the data, in reality, obey the long-tailed distribution, which consequently leads to traditional music recommendation systems recommending a lot of popular music that users do not like on a specific occasion. To solve these problems, we propose a heterogeneous knowledge-based attentive neural network model for short-term music recommendations. First, we collect three types of data for modeling entities in user–music interaction network, i.e., graphic, textual, and visual data, and then embed them into high-dimensional spaces using the TransR, distributed memory version of paragraph vector, and variational autoencoder methods, respectively. The concatenation of these embedding results is an abstract representation of the entity. Based on this, a recurrent neural network with an attention mechanism is built, which is capable of obtaining users’ preferences in the current session and consequently making recommendations. The experimental results show that our proposed approach outperforms the current state-of-the-art short-term music recommendation systems on one real-world dataset. In addition, it can also recommend more relatively unpopular songs compared with classic models.