Boosting Image Forgery Detection using Resampling Features and Copy-move analysis

Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods. While resampling detection algorithms are effective in detecting splicing and resampling, copy-move detection algorithms excel in detecting cloning and region removal. In this paper, we combine these complementary approaches in a way that boosts the overall accuracy of image manipulation detection. We use the copy-move detection method as a pre-filtering step and pass those images that are classified as untampered to a deep learning based resampling detection framework. Experimental results on various datasets including the 2017 NIST Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and tampered images shows that there is a consistent increase of 8%-10% in detection rates, when copy-move algorithm is combined with different resampling detection algorithms

Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries

With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy-clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture which utilizes resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts like JPEG quality loss, upsampling, downsampling, rotation, and shearing. The proposed network exploits larger receptive fields (spatial maps) and frequency domain correlation to analyze the discriminative characteristics between manipulated and non-manipulated regions by incorporating encoder and LSTM network. Finally, decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. With predicted mask provided by final layer (softmax) of the proposed architecture, end-to-end training is performed to learn the network parameters through back-propagation using ground-truth masks. Furthermore, a large image splicing dataset is introduced to guide the training process. The proposed method is capable of localizing image manipulations at pixel level with high precision, which is demonstrated through rigorous experimentation on three diverse datasets

Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis

The amount of digital imagery recorded has recently grown exponentially, and with the advancement of software, such as Photoshop or Gimp, it has become easier to manipulate images. However, most images on the internet have not been manipulated and any automated manipulation detection algorithm must carefully control the false alarm rate. In this paper we discuss a method to automatically detect local resampling using deep learning while controlling the false alarm rate using a-contrario analysis. The automated procedure consists of three primary steps. First, resampling features are calculated for image blocks. A deep learning classifier is then used to generate a heatmap that indicates if the image block has been resampled. We expect some of these blocks to be falsely identified as resampled. We use a-contrario hypothesis testing to both identify if the patterns of the manipulated blocks indicate if the image has been tampered with and to localize the manipulation. We demonstrate that this strategy is effective in indicating if an image has been manipulated and localizing the manipulations.Comment: arXiv admin note: text overlap with arXiv:1802.0315

Intelligent Side Information Generation in Distributed Video Coding

Distributed video coding (DVC) reverses the traditional coding paradigm of complex encoders allied with basic decoding to one where the computational cost is largely incurred by the decoder. This is attractive as the proven theoretical work of Wyner-Ziv (WZ) and Slepian-Wolf (SW) shows that the performance by such a system should be exactly the same as a conventional coder. Despite the solid theoretical foundations, current DVC qualitative and quantitative performance falls short of existing conventional coders and there remain crucial limitations. A key constraint governing DVC performance is the quality of side information (SI), a coarse representation of original video frames which are not available at the decoder. Techniques to generate SI have usually been based on linear motion compensated temporal interpolation (LMCTI), though these do not always produce satisfactory SI quality, especially in sequences exhibiting non-linear motion. This thesis presents an intelligent higher order piecewise trajectory temporal interpolation (HOPTTI) framework for SI generation with original contributions that afford better SI quality in comparison to existing LMCTI-based approaches. The major elements in this framework are: (i) a cubic trajectory interpolation algorithm model that significantly improves the accuracy of motion vector estimations; (ii) an adaptive overlapped block motion compensation (AOBMC) model which reduces both blocking and overlapping artefacts in the SI emanating from the block matching algorithm; (iii) the development of an empirical mode switching algorithm; and (iv) an intelligent switching mechanism to construct SI by automatically selecting the best macroblock from the intermediate SI generated by HOPTTI and AOBMC algorithms. Rigorous analysis and evaluation confirms that significant quantitative and perceptual improvements in SI quality are achieved with the new framework

Age-associated Collagen Crosslinking and its Role in Skeletal Muscle Regeneration

Advanced glycation end-products (AGEs) non-enzymatically accumulate on skeletal muscle collagen in old age via the Maillard reaction, causing an increase in intramuscular collagen and a stiffening of the muscle’s microenvironment. AGEs abrogate muscle regeneration through stiffening the muscle stem cell (MuSC) microenvironment and by binding to the receptor for advanced glycation end products (RAGE). Stiffer substrates promote MuSC proliferation at the expense of differentiation, and soluble AGEs are known to abrogate myogenic differentiation. Previously our group has demonstrated that decellularized muscle matrix (DMM), a type of extracellular matrix (ECM) scaffold extracted from skeletal muscle, encourages regeneration in a challenging rat volumetric muscle loss (VML) injury. Clinically, most human tissue for organ transplantation is sourced from older donors. My dissertation addresses whether old age is an important factor for DMM, and if this concern is AGE dependent. We isolated DMM from an aged murine model, and proved that AGE cross-links are present and that they are associated with increased stiffness. Further, we demonstrate that AGE-cross-linked collagen is stiffer, and disrupts myoblasts’ proliferation and differentiation in a RAGE-dependent manner. Curiously, AGE cross-links reduced RAGE in myoblasts, and RAGE inhibition shut down late myogenesis. Impressively, when myoblasts were challenged with the RAGE agonist S100b, myofiber formation was restored. We next proved that RAGE is significantly regulated in VML injuries, and we could regulate this with adipose-derived stromal cell delivery. Interestingly, AGEs were reduced in VML injuries, most likely due to an increase in new collagen deposition. Finally, we proved that an AGE-laden DMM disrupts muscle regeneration in a VML model and promotes inflammation while downregulating ECM synthesis. This was associated with upregulations in the AGE receptors RAGE and Galectin-3. Altogether, this dissertation provides strong evidence that age matters in the clinical translation of DMM, and AGEs are a prime target for rejuvenation therapies in skeletal muscle aging. Also, future study is warranted into the role that S100b can play in countering the AGE-RAGE axis in old age

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

The Role of Sox4 in Ocular Morphogenesis and Retinal Differentiation

Visual impairment ranges from mild forms that can be corrected with glasses to more severe cases that result in permanent loss of vision. Microphthalmia, anophthalmia, and coloboma (collectively referred to as MAC) account for 11% of cases of pediatric blindness and are a result of improper ocular morphogenesis. Retinitis Pigmentosa (RP) is a retinal degenerative disease that affects 1 in 3000 people worldwide. It is a progressive disorder that initially begins with loss of vision in low light settings due to rod photoreceptor degeneration but progresses to complete blindness upon loss of cone photoreceptors. Currently, there is no cure for either MAC or RP. Further insight into the essential components of ocular morphogenesis and the generation of retinal neurons could provide the base of knowledge needed for better patient screening and treatments like cell therapies. The transcription factor Sox4 has previously been implicated as an important factor in both ocular morphogenesis and retinal development. Studies in humans, mice, zebrafish, and Xenopus have all linked Sox4 to microphthalmia and coloboma. Additional studies suggest a role for Sox4 in the generation of specific retinal neurons. Interestingly, in zebrafish, the absence of maternal sox4 transcripts in the developing embryo results in both microphthalmia and a reduction of rod photoreceptors. This suggests that Sox4 has a critical role early in specification of the eyefield that influences later retinal differentiation, however the precise functions of Sox4 during vertebrate ocular morphogenesis and retinal cell type differentiation remain unclear. The studies presented in this Dissertation provide new insights into the role of Sox4 in eye development. Chapter 1 of this dissertation presents a review of ocular morphogenesis, retinal development, and what is currently known about the function of SoxC transcription factors and particularly Sox4 in embryonic and ocular development. In Chapter 2, a method to visualize ocular morphogenesis in living zebrafish embryos with high spatial and temporal resolution is demonstrated. Chapter 3 describes a detailed characterization of the ocular phenotypes of zebrafish sox4 mutants, and an in-depth analysis into the role Sox4 plays in both ocular morphogenesis and retinal differentiation. In vivo time lapse imaging, assays to assess cell proliferation and cell death, and immunohistochemistry to detect retinal cell types were used to characterize the phenotypes of microphthalmia and a reduction of rod photoreceptors in the sox4 mutants. Furthermore, scRNA-seq was used to address if there is any heterogeneity prior to ocular morphogenesis that may affect later retinal differentiation. Chapter 4 will address the impact of findings in the sox4 mutants, and the suggested future directions for this project. Finally, an appendix chapter will include additional data about a possible role for Sox4 in neural crest cells

Stabilization and Imaging of Cohesionless Soil Specimens

abstract: This dissertation describes development of a procedure for obtaining high quality, optical grade sand coupons from frozen sand specimens of Ottawa 20/30 sand for image processing and analysis to quantify soil structure along with a methodology for quantifying the microstructure from the images. A technique for thawing and stabilizing frozen core samples was developed using optical grade Buehler® Epo-Tek® epoxy resin, a modified triaxial cell, a vacuum/reservoir chamber, a desiccator, and a moisture gauge. The uniform epoxy resin impregnation required proper drying of the soil specimen, application of appropriate confining pressure and vacuum levels, and epoxy mixing, de-airing and curing. The resulting stabilized sand specimen was sectioned into 10 mm thick coupons that were planed, ground, and polished with progressively finer diamond abrasive grit levels using the modified Allied HTP Inc. polishing method so that the soil structure could be accurately quantified using images obtained with the use of an optical microscopy technique. Illumination via Bright Field Microscopy was used to capture the images for subsequent image processing and sand microstructure analysis. The quality of resulting images and the validity of the subsequent image morphology analysis hinged largely on employment of a polishing and grinding technique that resulted in a flat, scratch free, reflective coupon surface characterized by minimal microstructure relief and good contrast between the sand particles and the surrounding epoxy resin. Subsequent image processing involved conversion of the color images first to gray scale images and then to binary images with the use of contrast and image adjustments, removal of noise and image artifacts, image filtering, and image segmentation. Mathematical morphology algorithms were used on the resulting binary images to further enhance image quality. The binary images were then used to calculate soil structure parameters that included particle roundness and sphericity, particle orientation variability represented by rose diagrams, statistics on the local void ratio variability as a function of the sample size, and the local void ratio distribution histograms using Oda's method and Voronoi tessellation method, including the skewness, kurtosis, and entropy of a gamma cumulative probability distribution fit to the local void ratio distribution.Dissertation/ThesisM.S. Civil Engineering 201