Forensic research on detecting seam carving in digital images

Digital images have been playing an important role in our daily life for the last several decades. Naturally, image editing technologies have been tremendously developed due to the increasing demands. As a result, digital images can be easily manipulated on a personal computer or even a cellphone for many purposes nowadays, so that the authenticity of digital images becomes an important issue. In this dissertation research, four machine learning based forensic methods are presented to detect one of the popular image editing techniques, called ‘seam carving’. To reveal seam carving applied to uncompressed images from the perspective of energy distribution change, an energy based statistical model is proposed as the first work in this dissertation. Features measured global energy of images, remaining optimal seams, and noise level are extracted from four local derivative pattern (LDP) domains instead of from the original pixel domain to heighten the energy change caused by seam carving. A support vector machine (SVM) based classifier is employed to determine whether an image has been seam carved or not. In the second work, an advanced feature model is presented for seam carving detection by investigating the statistical variation among neighboring pixels. Comprised with three types of statistical features, i.e., LDP features, Markov features, and SPAM features, the powerful feature model significantly improved the state-of-the-art accuracy in detecting low carving rate seam carving. After the feature selection by utilizing SVM based recursive feature elimination (SVM-RFE), with a small amount of features selected from the proposed model the overall performance is further improved. Combining above mentioned two works, a hybrid feature model is then proposed as the third work to further boost the accuracy in detecting seam carving at low carving rate. The proposed model consists of two sets of features, which capture energy change and neighboring relationship variation respectively, achieves remarkable performance on revealing seam carving, especially low carving rate seam carving, in digital images. Besides these three hand crafted feature models, a deep convolutional neural network is designed for seam carving detection. It is the first work that successfully utilizes deep learning technology to solve this forensic problem. The experimental works demonstrate their much more improved performance in the cases where the amount of seam carving is not serious. Although these four pieces of work move the seam carving detection ahead substantially, future research works with more advanced statistical model or deep neural network along this line are expected

Global Auto-regressive Depth Recovery via Iterative Non-local Filtering

Existing depth sensing techniques have many shortcomings in terms of resolution, completeness, and accuracy. The performance of 3-D broadcasting systems is therefore limited by the challenges of capturing high-resolution depth data. In this paper, we present a novel framework for obtaining high-quality depth images and multi-view depth videos from simple acquisition systems. We first propose a single depth image recovery algorithm based on auto-regressive (AR) correlations. A fixed-point iteration algorithm under the global AR modeling is derived to efficiently solve the large-scale quadratic programming. Each iteration is equivalent to a nonlocal filtering process with a residue feedback. Then, we extend our framework to an AR-based multi-view depth video recovery framework, where each depth map is recovered from low-quality measurements with the help of the corresponding color image, depth maps from neighboring views, and depth maps of temporally adjacent frames. AR coefficients on nonlocal spatiotemporal neighborhoods in the algorithm are designed to improve the recovery performance. We further discuss the connections between our model and other methods like graph-based tools, and demonstrate that our algorithms enjoy the advantages of both global and local methods. Experimental results on both the Middleburry datasets and other captured datasets finally show that our method is able to improve the performances of depth images and multi-view depth videos recovery compared with state-of-the-art approaches

Negative Group Velocity in the Absence of Absorption Resonance

Scientific community has well recognized that a Lorentzian medium exhibits anomalous dispersion behavior in its resonance absorption region. To satisfy the Krammers-Kronig relation, such an anomalous region has to be accompanied with significant loss, and thus, experimental observations of negative group velocity in this region generally require a gain-assisted approach. In this letter, we demonstrate that the negative group velocity can also be observed in the absence of absorption resonance. We show that the k-surface of a passive uniaxial Lorentzian medium undergoes a distortion near the plasma frequency. This process yields an anomalous dispersion bandwidth that is far away from the absorption resonance region, and enables the observation of negative group velocity at the plasma frequency band. Introducing anomalous dispersion in a well-controlled manner would greatly benefit the research of ultrafast photonics and find potential applications in optical delay lines, optical data storage and devices for quantum information processing

SOOD: Towards Semi-Supervised Oriented Object Detection

Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years. However, existing SSOD approaches mainly focus on horizontal objects, leaving multi-oriented objects that are common in aerial images unexplored. This paper proposes a novel Semi-supervised Oriented Object Detection model, termed SOOD, built upon the mainstream pseudo-labeling framework. Towards oriented objects in aerial scenes, we design two loss functions to provide better supervision. Focusing on the orientations of objects, the first loss regularizes the consistency between each pseudo-label-prediction pair (includes a prediction and its corresponding pseudo label) with adaptive weights based on their orientation gap. Focusing on the layout of an image, the second loss regularizes the similarity and explicitly builds the many-to-many relation between the sets of pseudo-labels and predictions. Such a global consistency constraint can further boost semi-supervised learning. Our experiments show that when trained with the two proposed losses, SOOD surpasses the state-of-the-art SSOD methods under various settings on the DOTA-v1.5 benchmark. The code will be available at https://github.com/HamPerdredes/SOOD.Comment: Accepted to CVPR 2023. Code will be available at https://github.com/HamPerdredes/SOO

Finite element analysis of rapid canine retraction through reducing resistance and distraction

Objective: The aims of this study were to compare different surgical approaches to rapid canine retraction by designing and selecting the most effective method of reducing resistance by a three-dimensional finite element analysis. Material and Methods: Three-dimensional finite element models of different approaches to rapid canine retraction by reducing resistance and distraction were established, including maxillary teeth, periodontal ligament, and alveolar. The models were designed to dissect the periodontal ligament, root, and alveolar separately. A 1.5 N force vector was loaded bilaterally to the center of the crown between first molar and canine, to retract the canine distally. The value of total deformation was used to assess the initial displacement of the canine and molar at the beginning of force loading. Stress intensity and force distribution were analyzed and evaluated by Ansys 13.0 through comparison of equivalent (von Mises) stress and maximum shear stress. Results: The maximum value of total deformation with the three kinds of models occurred in the distal part of the canine crown and gradually reduced from the crown to the apex of the canine; compared with the canines in model 3 and model 1, the canine in model 2 had the maximum value of displacement, up to 1.9812 mm. The lowest equivalent (von Mises) stress and the lowest maximum shear stress were concentrated mainly on the distal side of the canine root in model 2. The distribution of equivalent (von Mises) stress and maximum shear stress on the PDL of the canine in the three models was highly concentrated on the distal edge of the canine cervix. . Conclusions: Removal of the bone in the pathway of canine retraction results in low stress intensity for canine movement. Periodontal distraction aided by surgical undermining of the interseptal bone would reduce resistance and effectively accelerate the speed of canine retraction

A Novel Signal Transduction Pathway that Modulates <i>rhl</i> Quorum Sensing and Bacterial Virulence in <i>Pseudomonas aeruginosa</i>

The rhl quorum-sensing (QS) system plays critical roles in the pathogenesis of P. aeruginosa. However, the regulatory effects that occur directly upstream of the rhl QS system are poorly understood. Here, we show that deletion of gene encoding for the two-component sensor BfmS leads to the activation of its cognate response regulator BfmR, which in turn directly binds to the promoter and decreases the expression of the rhlR gene that encodes the QS regulator RhlR, causing the inhibition of the rhl QS system. In the absence of bfmS, the Acka-Pta pathway can modulate the regulatory activity of BfmR. In addition, BfmS tunes the expression of 202 genes that comprise 3.6% of the P. aeruginosa genome. We further demonstrate that deletion of bfmS causes substantially reduced virulence in lettuce leaf, reduced cytotoxicity, enhanced invasion, and reduced bacterial survival during acute mouse lung infection. Intriguingly, specific missense mutations, which occur naturally in the bfmS gene in P. aeruginosa cystic fibrosis (CF) isolates such as DK2 strains and RP73 strain, can produce BfmS variants (BfmSL181P, BfmSL181P/E376Q, and BfmSR393H) that no longer repress, but instead activate BfmR. As a result, BfmS variants, but not the wild-type BfmS, inhibit the rhl QS system. This study thus uncovers a previously unexplored signal transduction pathway, BfmS/BfmR/RhlR, for the regulation of rhl QS in P. aeruginosa. We propose that BfmRS TCS may have an important role in the regulation and evolution of P. aeruginosa virulence during chronic infection in CF lungs.</p

Concept for a Future Super Proton-Proton Collider

Following the discovery of the Higgs boson at LHC, new large colliders are being studied by the international high-energy community to explore Higgs physics in detail and new physics beyond the Standard Model. In China, a two-stage circular collider project CEPC-SPPC is proposed, with the first stage CEPC (Circular Electron Positron Collier, a so-called Higgs factory) focused on Higgs physics, and the second stage SPPC (Super Proton-Proton Collider) focused on new physics beyond the Standard Model. This paper discusses this second stage.Comment: 34 pages, 8 figures, 5 table

Protein quality control: the who’s who, the where’s and therapeutic escapes

In cells the quality of newly synthesized proteins is monitored in regard to proper folding and correct assembly in the early secretory pathway, the cytosol and the nucleoplasm. Proteins recognized as non-native in the ER will be removed and degraded by a process termed ERAD. ERAD of aberrant proteins is accompanied by various changes of cellular organelles and results in protein folding diseases. This review focuses on how the immunocytochemical labeling and electron microscopic analyses have helped to disclose the in situ subcellular distribution pattern of some of the key machinery proteins of the cellular protein quality control, the organelle changes due to the presence of misfolded proteins, and the efficiency of synthetic chaperones to rescue disease-causing trafficking defects of aberrant proteins