A Formula Adaptive Pixel Pair Matching Steganography Algorithm

is driven by edge adaptive technique. The cover image is scanned first to explore the edges of the image and is used as the candidate region to perform data hiding using pixel pair matching. The basic idea of pixel pair matching (PPM) is to use the value of a pixel pair as a reference coordinate, and search a coordinate in the neighborhood set of this pixel pair according to a given message digit. The pixel pair is then replaced by the searched coordinate to conceal the digit. The method uses only sharper edge regions while keeping the other smoother regions as they are. In contrast to the existing methods based on PPM, the masking driven approach has lower distortion for various payloads. The proposed method is expected to provide better performance than the existing techniques based on LSB and also secure against well-known steganalysis techniques.

Pixel pair matching (PPM) is a method for hiding secret data inside a cover pixel using two pixel pair co-ordinates. One pixel pair is used as reference co-ordinate and the other is used as the reference co-ordinate. It is one form of substitution methods. Adaptive pixel pair matching (APPM) is the one variant of the PPM where the former differs from the latter in the extraction function it utilizes. APPM achieves good embedding capacity. Encrypting the cover pixel before data embedding enhances the system security. Thus, this paper aims in embedding secret information into an encrypted cover image. Combined form of Cryptography and Steganography is the added feature of this paper. Stream Ciphered cover image is subjected to the APPM data embedding. The key that is used at the transmitter for encryption is used at the receiver for decryption. The decrypted image is then extracted to obtain the hidden information.

Adaptive optimization strategy of air supply for automotive polymer electrolyte membrane fuel cell in life cycle

Wireless power transfer (WPT) systems’ efficiency is significantly impacted by non-monotonic variations in the coupling coefficient. For very short distances or strong-coupling cases, the WPT efficiency is minimal at the natural resonant frequency, with two peaks around this frequency, known as the frequency splitting phenomenon. On the other hand, WPT capability decreases for long distances or weak coupling cases. Therefore, adaptive matching is required for WPT systems with varying distances, like wireless charging systems for electric vehicles (EVs). This paper first presents a detailed analysis of the frequency splitting phenomenon by studying the root locations of the WPT system’s transfer function. Then, a real-time fixed-frequency adaptive impedance matching (IM) method is proposed, in which the amplitude and phase of the input impedance is estimated using the average active power, the average reactive power, and the amplitude of input voltage. Unlike traditional search-and-find techniques, the proposed method calculates the optimal IM network parameters only in a single iteration, which improves the convergent speed. A scaled-down 20-Watt prototype controlled by the TMSF2812 is fabricated and used to validate the effectiveness of the proposed method over a wide range of coil-to-coil distances.

The recently introduced adaptive force matching (AFM) method is used to develop a significantly improved pair-wise nonpolarizable potential for water. A rigid version of the potential is also presented to enable larger time steps for biological simulations. In this work, it is demonstrated that the AFM method can be used to systematically assess the importance of each functional term during the construction of a force field. For a water potential, it is established that a single off-atom charge center (M) in the plane of water outperforms two out-of-plane charge sites for reproducing intermolecular forces. The four-site pair-wise nonpolarizable force field developed in this work rivals some of the most sophisticated polarizable models in terms of reproducing accurate ab initio forces. The force fields are parameterized to perform best in the temperature range from 0 to 40°C. Equilibrium and dynamical properties calculated with the flexible and rigid force fields are in good agreement with experimental results. For the flexible model, the agreement improves when path integral simulation is performed. These force fields provide high-quality results at a very low computational cost and are thus well suited to atomistic scale biological simulations. The AFM method provides a mechanism for selecting important terms in force field expressions and is a very promising tool for producing accurate force fields in condensed phases.

Dense feature extraction is becoming increasingly popular in face recognition tasks. Systems based on this approach have demonstrated impressive performance in a range of challenging scenarios. However, improvements in discriminative power come at a computational cost and with a risk of over-fitting. In this paper, we propose a new approach to dense feature extraction for face recognition, which consists of two steps. First, an encoding scheme is devised that compresses high-dimensional dense features into a compact representation by maximizing the intrauser correlation. Second, we develop an adaptive feature matching algorithm for effective classification. This matching method, in contrast to the previous methods, constructs and chooses a small subset of training samples for adaptive matching, resulting in further performance gains. Experiments using several challenging face databases, including labeled Faces in the Wild data set, Morph Album 2, CUHK optical-infrared, and FERET, demonstrate that the proposed approach consistently outperforms the current state of the art.

In this paper, we propose a method to detect and recover the occluded parts of face images using the correlation between pairs of pixels. In the training stage, correlation coefficients between every pairs of pixels are calculated using the occlusion-free training face images. Once a new face image is shown, the occluded area is detected and recovered using correlation coefficients obtained in the training stage. We compare the performance of the proposed method with the conventional method based on PCA. The results show that the proposed method detects and recovers occluded area with much smaller noises than the conventional PCA based method.

WITHDRAWN: Contrasting the performance metrics of discrete transformations on digital image steganography using artificial intelligence

A data hiding scheme using pixel value differencing and improving exploiting modification directions

A novel edge orientation determination algorithm is proposed. It can detect edges and decide very precisely their orientations based on a pixel pair matching technique. Three image edge structure measures, profile diversity, edge convexity and edge continuity, are designed to facilitate the determination process. The basic algorithm outputs an integer-pixel orientation vector for each detected edge and this orientation vector can be refined to subpixel accuracy by a polynomial fitting method. Simulation results are included to show the advantages of this approach.

It is quite an important and challenging problem for change detection (CD) from heterogeneous remote sensing images. The images obtained from different sensors (i.e., synthetic aperture radar (SAR) & optical camera) characterize the distinct properties of objects. Thus, it is impossible to detect changes by direct comparison of heterogeneous images. In this article, a new unsupervised change detection (USCD) method is proposed based on image translation. The cycle-consistent adversarial networks (CycleGANs) are employed to learn the subimage to subimage mapping relation using the given pair (i.e., before and after the event) of heterogeneous images from which the changes will be detected. Then, we can translate one image (e.g., SAR) from its original feature space (e.g., SAR) to another space (e.g., optical). By doing this, the pair of images can be represented in a common feature space (e.g., optical). The pixels with close pattern values in the before-event image may have quite different values in the after-event image if the change happens on some ones. Thus, we can generate the difference map between the translated before-event image and the original after-event image. Then, the difference map is divided into changed and unchanged parts. However, these detection results are not very reliable. We will select some significantly changed and unchanged pixel pairs from the two parts with the clustering technique (i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means). These selected pixel pairs are used to learn a binary classifier, and the other pixel pairs will be classified by this classifier to obtain the final CD results. Experimental results on different real datasets demonstrate the effectiveness of the proposed USCD method compared with several other related methods.

This paper presents an eye gaze estimation system which robust against various users. Our method utilizes an IR camera mounted on glass to allow user’s movement. Pupil knowledge such as shape, size, location, and motion are used. This knowledge works based on the knowledge priority. Pupil appearance such as size, color, and shape are used as the first priority. When this step fails, then pupil is estimated based on its location as second priority. When all steps fail, then we estimate pupil based on its motion as the last priority. The aim of this proposed method is to make the system compatible for various user as well as to overcome problem associated with illumination changes and user movement. The proposed system is tested using several users with various race as well as nationality and the experiment result are compared to the well-known adaptive threshold method and template matching method. The proposed method shows good performance, robustness, accuracy and stability against illumination changes without any prior calibration.KeywordsGazeeye detectionpupilpupil knowledge

A dual image based reversible data hiding (RDH) method using pixel value difference (PVD) and exploiting modification direction (EMD) are introduced in this paper. First enlarge the cover image size and then select a pair of pixel for data embedding. We embed 4 bits secret message through pixel value difference and 3 bits secret message using embedding function of exploiting modification direction within a pixel pair. We obtain two modified pairs of pixel which contain 7 bits secret message. We then distribute these pixel pairs among dual stego images depending on a shared secret key (K). The recipient successfully obtain secret message and retrieve original image using same shared secret key. We compared our proposed method with other existing techniques and find out averagely good results with respective to payload.

In this paper, a monocular 3D dense reconstruction technique based on optical flow feedback is proposed to achieve fast and accurate 3D stereoscopic modeling in the real scene. Corresponding pixel pairs are robustly matched by inter-frame optical flow fields, and the five-point algorithm is employed to determine relative pose of the moving camera, based on which sparse point cloud is generated and initial coarse mesh is built. In the proposed method, multi-view reconstruction is implemented from perspective of vision method on motion analysis. The reconstruction model is fed back to the reconstruction process, and the model is deformed by utilizing the bias-driven of each view. The coarse and inaccurate original mesh surface is adjusted to the exact surface through a dense non-rigid deformation. Under the Compute Unified Device Architecture (CUDA), the reconstruction algorithm is optimized in parallel mode by using the Graphic Processing Unit (GPU) hardware, and real-time performance of the reconstruction algorithm is significantly improved. The experimental results obtained in realistic indoor scenario demonstrate the effectiveness and accuracy of the proposed algorithm.

Data hiding is a technique that embeds a secret message into a cover medium and transfers the hidden information in the secret message to the recipient. In the past, several data hiding methods based on magic matrix have used various geometrical shapes to transmit secret data. The embedding capacity achieved in these methods was often limited due to simple geometrical layouts. This paper proposes a data hiding scheme based on a double-layer octagon-shaped shell matrix. Compared to previous octagon-shaped data hiding methods, the proposed method embeds a total of 7 bits in each pixel pair, reaching an embedding capacity of 3.5 bits per pixel (bpp). Experimental results show that the proposed scheme has a higher embedding capacity compared to other irreversible data hiding schemes. Using the proposed method, it is possible to maintain the Peak Signal to Noise Ratio (PSNR) within an acceptable range with the embedding time less than 2 s.