This paper proposes a novel steganographic method that employs a feedback mechanism to improve the efficiency and stealth of data hiding within the Discrete Cosine Transform (DCT) coefficients of JPEG images. This method enhances the correlation between the hidden message and the cover image, while minimizing the perceptible changes to the image. The system starts by dividing the cover image into blocks and applying DCT to each. It then evaluates the correlation between the hidden message and the DCT coefficients to identify potential data embedding points. A trained decision rules algorithm then chooses the optimal data embedding technique, considering factors like the size and location of the DCT coefficient within image blocks. Different embedding techniques are employed. The system subsequently generates feedback based on metrics such as image quality and data detectability, refining the decision ruls's effectiveness over time. By employing this dynamic approach, our system adaptively improves the data hiding process, enhancing capacity and minimizing detectability. This work opens new doors in the realm of steganography, presenting an intelligent system capable of adaptively embedding data with optimized stealth and efficiency.
The BGU CubeSat satellite is from a class of low-cost, compact satellites. Its dimensions are 10×10×30 cm. It is equipped with a low resolution 256×320 pixels short wave infrared (SWIR) camera at the 1.55-1.7mm wavelength band. Images are transmitted in bursts of tens of images at a time with few pixel shifts from the first image to the last. Each image burst is suitable for Multiple Image Super Resolution (MISR) enhancements. MISR can construct a high-resolution (HR) image from several low-resolution (LR) images yielding an image that can resolve more details that are crucial for research in remote sensing. In this research, we verify the applicability of SOTA deep learning MISR models that were developed following the publication of the PROBA-V MISR satellite dataset at the visible red and near IR. Our SWIR multiple images differ from PROBA-V by the spectral band and by the method of collecting multiple images of the exact location. Our imagery data is acquired by a burst of very close temporal images. PROBA-V revisits the satellite at a period smaller than 30 days, assuming the soil dryness is about the same. We compare the results of Single Image Super-Resolution (SISR) and MISR techniques to "off-the-shelf" products. The quality of the super-resolved images is compared by nonreference metrics suitable for remote sensing applications and by experts' visual inspection. Unlike remarkable achievements by the GAN technique that can achieve very appealing results that are not always faithful to the original ground truth, the super-resolved images should preserve the original details as much as possible for further scientific remote sensing analysis.
Standard video compression algorithms use multiple “Modes”, which are various linear combinations of pixels for prediction of their neighbors within image Macro-Blocks (MBs). In this research, we are using Deep Neural Networks (DNN) with supervised learning to predict block pixels. Using DNNs and employing intra-block pixel values’ calculations that penetrate into the block, we manage to obtain improved predictions that yield up to 200% reduction of residual block errors. However, using intra-block pixels for predictions brings upon interesting tradeoffs between prediction errors and quantization errors. We explore and explain these tradeoffs for two different DNN types. We further discovered that it is possible to achieve a larger dynamic range of quantization parameter (Qp) and thus reach lower bit-rates than standard modes, which already saturate at these Qp levels. We explore this phenomenon and explain its reasoning.
Image steganography is the art of hiding information in a cover image in such a way that a third party does not notice the hidden information. This paper presents a novel technique for image steganography in the spatial domain. The new method hides and recovers hidden information of substantial length within digital imagery, while maintaining the size and quality of the original image. The image gradient is used to generate a saliency image, which represent the energy of each pixel in the image. Pixels with higher energy are more salient and they are valuable for hiding data since their visual impairment is low. From the saliency image, a cumulative maximum energy matrix is created; this matrix is used to generate horizontal seams that pass over the maximum energy path. By embedding the secret bits of information along the seams, a stego-image is created which contains the hidden message. In the stegoimage, we ensure that the hidden data is invisible, with very small perceived image quality degradation. The same algorithms are used to reconstruct the hidden message from the stego-image. Experiments have been conducted using two types of image and two types of hidden data to evaluate the proposed technique. The experimental results show that the proposed algorithm has a high capacity and good invisibility, with a Peak Signal-to-Noise Ratio (PSNR) of about 70, and a Structural SIMilarity index (SSIM) of about 1.
We analyze the connection between viewer-perceived quality and encoding schemes. The encoding schemes depend on transmission bit-rate, MPEG compression depth, frame size and frame rate in a constant bit-rate (CBR) video transmission of a MPEG-2 video sequence. The compressed video sequence is transmitted over a lossy communication network with quality of service (QoS) and a certain Internet (IP) loss model. On the end-user side, viewer-perceived quality depends on changes in the network conditions, the video compression, and the video content complexity. We demonstrate that, when jointly considering the impact of coding bit rate, packet loss, and video complexity, there is an optimal encoding scheme, which also depends on the video content. We use a set of subjective tests to demonstrate that this optimal encoding scheme maximizes the viewer-perceived quality.
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