Today convincing digital forgery can be created without master learning of image editing software. These fake pictures over exceptionally quick media may cause extreme results in the public arena. Passive digital image forensic is an area which uncovers these problems. Since JPEG compression deals with 8 × 8 DCT matrix it makes its own fingerprint which can be utilized to distinguish further forgeries in the picture. In this paper, we have proposed a technique which automatically locates forgery in the image based on histogram of DCT coefficient factors, called as factor histogram. When image undergoes aligned double compression this factor histogram shows peak at current quantization step as well as primary quantization step. Our algorithm searches for absence of such double maxima in block-wise factor histogram to identify tampered region. This method can find copy-move, copy-paste as well as pre-processed forgeries such as rotation and scaling.

Facebook images get distributed within a fraction of a second, which hackers may tamper and redistribute on cyberspace. JPEG fingerprint based tampering detection techniques have major scope in tampering localization within standard JPEG images. The majority of these algorithms fails to detect tampering created using Facebook images. Facebook utilizes down-sampling followed by compression, which makes difficult to locate tampering created with these images. In this paper, the authors have proposed the tampering localization algorithm, which locates tampering created with the images downloaded from Facebook. The algorithm uses Factor Histogram of DCT coefficients at first 15 modes to find primary quantization steps. The image is divided into BXB overlapping blocks and each block is processed individually. Votes cast by these modes for conceivable tampering are collected at every pixel position and the ones above threshold are used to form different regions. High density voted region is proclaimed as tampered region.

This paper represents novel iris recognition technique which uses textural and topological features.

Digital image tampering operations destroy inbuilt fingerprints and create own new fingerprint in the tampered region. Considering the Internet speed and storage space, most of the images are circulated in the JPEG format. In a single compressed JPEG image, the first digits of DCT coefficients follow a logarithmic distribution. This distribution is not followed by DCT coefficients of DCT grid aligned double compressed images. In a tampered image, the major portion of the original JPEG image is aligned double JPEG compressed. Hence, untampered region does not follow this logarithmic distribution. Due to the nonalignment of DCT compression grids, tampered region still follows this logarithmic distribution. Many tampering localization techniques have investigated this fingerprint, but the majority of them uses SVM classifier, specifically trained for the respective primary and secondary compression qualities of the test images. The efficiency of these classifiers is dependent on the knowledge of tampered image compression history. Hence, these approaches are not fully automated. In this paper, we have investigated a method, which does not require prior compression quality knowledge. Our experimental analysis shows that the addition of Gaussian noise can make the probability distribution of an aligned double compressed image similar to a nonaligned double compressed image. We divided the test image and its Gaussian version into sub-images and clustered them using K-means clustering algorithm. The application of K-means clustering algorithm does not require compression quality knowledge. This makes our approach more practical as compared to the other first digit probability distribution-based algorithms. The proposed algorithm gives compatible performance with the other approaches, based on different JPEG fingerprints.

Clinical research comprises participation from patients. Often there are concerns of enrolment from patients. Hence, it has to face various challenges related to personal data, such as data sharing, privacy and reproducibility, etc. Patients and researchers need to track a set plan called study protocol. This protocol spans through various stages such as registration, collection and analysis of data, report generation, and finally, results in publication of findings. The Blockchain technology has emerged as one of the possible solutions to these challenges. It has a potential to address all the problem associated with clinical research. It provides the comfort for building transparent, secure services relying on trusted third party. This technology enables one to share the control of the data, security, and the parameters with a single patient or a group of patients or any other stakeholders of clinical trial. This chapter addresses the use of blockchain in execution of secure and trusted clinical trials.