Texture based Image Splicing Forgery Recognition using a Passive Approach

With the growing usage of the internet in daily life along with the usage of dominant picture editing software tools in creating forged pictures effortlessly, make us lose trust in the authenticity of the images. For more than a decade, extensive research is going on in the Image forensic area that aims at restoring trustworthiness in images by bringing various tampering detection techniques. In the proposed method, identification of image splicing technique is introduced which depends on the picture texture analysis which characterizes the picture areas by the content of the texture. In this method, an image is characterized by the regions of their texture content. The experimental outcomes describe that the proposed method is effective to identify spliced picture forgery with an accuracy of 79.5%

A robust forgery detection method for copy-move and splicing attacks in images

Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy-move attacks, effortless, causing cybercrimes to be on the rise. While several models have been proposed in the literature for detecting these attacks, the robustness of those models has not been investigated when (i) a low number of tampered images are available for model building or (ii) images from IoT sensors are distorted due to image rotation or scaling caused by unwanted or unexpected changes in sensors' physical set-up. Moreover, further improvement in detection accuracy is needed for real-word security management systems. To address these limitations, in this paper, an innovative image forgery detection method has been proposed based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. First, images are divided into non-overlapping fixed size blocks and 2D block DCT is applied to capture changes due to image forgery. Then LBP is applied to the magnitude of the DCT array to enhance forgery artifacts. Finally, the mean value of a particular cell across all LBP blocks is computed, which yields a fixed number of features and presents a more computationally efficient method. Using Support Vector Machine (SVM), the proposed method has been extensively tested on four well known publicly available gray scale and color image forgery datasets, and additionally on an IoT based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples.This research was funded by Research Priority Area (RPA) scholarship of Federation University Australia

A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization

We propose a new algorithm for the reliable detection and localization of video copy-move forgeries. Discovering well crafted video copy-moves may be very difficult, especially when some uniform background is copied to occlude foreground objects. To reliably detect both additive and occlusive copy-moves we use a dense-field approach, with invariant features that guarantee robustness to several post-processing operations. To limit complexity, a suitable video-oriented version of PatchMatch is used, with a multiresolution search strategy, and a focus on volumes of interest. Performance assessment relies on a new dataset, designed ad hoc, with realistic copy-moves and a wide variety of challenging situations. Experimental results show the proposed method to detect and localize video copy-moves with good accuracy even in adverse conditions

On the Detection and Recognition of Television Commercials

TV commercials are interesting in many respects: advertisers and psychologists are interested in their influence on human purchasing habits, while parents might be interested in shielding their children from their influence. In this paper, two methods for detecting and extracting commercials in digital videos are described. The first method is based on statistics of measurable features and enables the detection of commercial blocks within TV broadcasts. The second method performs detection and recognition of known commercials with high accuracy. Finally, we show how both approaches can be combined into a self-learning system. Our experimental results underline the practicality of the methods

Measuring trustworthiness of image data in the internet of things environment

Internet of Things (IoT) image sensors generate huge volumes of digital images every day. However, easy availability and usability of photo editing tools, the vulnerability in communication channels and malicious software have made forgery attacks on image sensor data effortless and thus expose IoT systems to cyberattacks. In IoT applications such as smart cities and surveillance systems, the smooth operation depends on sensors’ sharing data with other sensors of identical or different types. Therefore, a sensor must be able to rely on the data it receives from other sensors; in other words, data must be trustworthy. Sensors deployed in IoT applications are usually limited to low processing and battery power, which prohibits the use of complex cryptography and security mechanism and the adoption of universal security standards by IoT device manufacturers. Hence, estimating the trust of the image sensor data is a defensive solution as these data are used for critical decision-making processes. To our knowledge, only one published work has estimated the trustworthiness of digital images applied to forensic applications. However, that study’s method depends on machine learning prediction scores returned by existing forensic models, which limits its usage where underlying forensics models require different approaches (e.g., machine learning predictions, statistical methods, digital signature, perceptual image hash). Multi-type sensor data correlation and context awareness can improve the trust measurement, which is absent in that study’s model. To address these issues, novel techniques are introduced to accurately estimate the trustworthiness of IoT image sensor data with the aid of complementary non-imagery (numeric) data-generating sensors monitoring the same environment. The trust estimation models run in edge devices, relieving sensors from computationally intensive tasks. First, to detect local image forgery (splicing and copy-move attacks), an innovative image forgery detection method is proposed based on Discrete Cosine Transformation (DCT), Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. Using Support Vector Machine (SVM), the proposed method is extensively tested on four well-known publicly available greyscale and colour image forgery datasets and on an IoT-based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples. Second, a robust trust estimation framework for IoT image data is proposed, leveraging numeric data-generating sensors deployed in the same area of interest (AoI) in an indoor environment. As low-cost sensors allow many IoT applications to use multiple types of sensors to observe the same AoI, the complementary numeric data of one sensor can be exploited to measure the trust value of another image sensor’s data. A theoretical model is developed using Shannon’s entropy to derive the uncertainty associated with an observed event and Dempster-Shafer theory (DST) for decision fusion. The proposed model’s efficacy in estimating the trust score of image sensor data is analysed by observing a fire event using IoT image and temperature sensor data in an indoor residential setup under different scenarios. The proposed model produces highly accurate trust scores in all scenarios with authentic and forged image data. Finally, as the outdoor environment varies dynamically due to different natural factors (e.g., lighting condition variations in day and night, presence of different objects, smoke, fog, rain, shadow in the scene), a novel trust framework is proposed that is suitable for the outdoor environments with these contextual variations. A transfer learning approach is adopted to derive the decision about an observation from image sensor data, while also a statistical approach is used to derive the decision about the same observation from numeric data generated from other sensors deployed in the same AoI. These decisions are then fused using CertainLogic and compared with DST-based fusion. A testbed was set up using Raspberry Pi microprocessor, image sensor, temperature sensor, edge device, LoRa nodes, LoRaWAN gateway and servers to evaluate the proposed techniques. The results show that CertainLogic is more suitable for measuring the trustworthiness of image sensor data in an outdoor environment.Doctor of Philosoph

Learning Rich Features for Image Manipulation Detection

Image manipulation detection is different from traditional semantic object detection because it pays more attention to tampering artifacts than to image content, which suggests that richer features need to be learned. We propose a two-stream Faster R-CNN network and train it endto- end to detect the tampered regions given a manipulated image. One of the two streams is an RGB stream whose purpose is to extract features from the RGB image input to find tampering artifacts like strong contrast difference, unnatural tampered boundaries, and so on. The other is a noise stream that leverages the noise features extracted from a steganalysis rich model filter layer to discover the noise inconsistency between authentic and tampered regions. We then fuse features from the two streams through a bilinear pooling layer to further incorporate spatial co-occurrence of these two modalities. Experiments on four standard image manipulation datasets demonstrate that our two-stream framework outperforms each individual stream, and also achieves state-of-the-art performance compared to alternative methods with robustness to resizing and compression.Comment: CVPR 2018 Camera Read

Development of an UAS for Earthquake Emergency Response and Its Application in Two Disastrous Earthquakes

To support humanitarian action after a disaster, we require reliable data like high-resolution satellite images for analyses aimed to define the damages of facilities and/or infrastructures. However, we cannot obtain satellite images in few days after an event. Thus, in situ surveys are preferred. Advances in unmanned aircraft system (UAS) have promoted them to become precious tools for capturing and assessing the extents and volume of damages. Safety, flexibility, low cost, and ease of operation make UAS suitable for disaster assessment. In this chapter, we developed an example of UAS for swiftly acquiring disaster information. With the selected fixed-wing UAS, we successfully performed data acquisition at specified scales. For the image analysis, we applied a photogrammetric workflow to deal with the very high resolution of the images obtained without ground control points. The results obtained from two destructive earthquakes demonstrated that the presented system plays a key role on the processes of investigating and gathering information about a disaster in the earthquake epicentral areas, like road detection, structural damage survey, secondary disaster investigation, and quick disaster assessment. It can effectively provide disaster information in hardly entered areas to salvation headquarters for rapidly developing the relief measures

Compound C inhibits nonsense-mediated RNA decay independently of AMPK

The nonsense mediated RNA decay (NMD) pathway safeguards the integrity of the transcriptome by targeting mRNAs with premature translation termination codons (PTCs) for degradation. It also regulates gene expression by degrading a large number of non-mutant RNAs (including mRNAs and noncoding RNAs) that bear NMD-inducing features. Consequently, NMD has been shown to influence development, cellular response to stress, and clinical outcome of many genetic diseases. Small molecules that can modulate NMD activity provide critical tools for understanding the mechanism and physiological functions of NMD, and they also offer potential means for treating certain genetic diseases and cancer. Therefore, there is an intense interest in identifying small-molecule NMD inhibitors or enhancers. It was previously reported that both inhibition of NMD and treatment with the AMPK-selective inhibitor Compound C (CC) induce autophagy in human cells, raising the possibility that CC may be capable of inhibiting NMD. Here we show that CC indeed has a NMD-inhibitory activity. Inhibition of NMD by CC is, however, independent of AMPK activity. As a competitive ATP analog, CC does not affect the kinase activity of SMG1, an essential NMD factor and the only known kinase in the NMD pathway. However, CC treatment down-regulates the protein levels of several NMD factors. The induction of autophagy by CC treatment is independent of ATF4, a NMD target that has been shown to promote autophagy in response to NMD inhibition. Our results reveal a new activity of CC as a NMD inhibitor, which has implications for its use in basic research and drug development