102 research outputs found

    Review on tools for image detection forgery

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    This paper defines the presently used methods and approaches in the domain of digital image forgery detection. A survey of a recent study is explored including an examination of the current techniques and passive approaches in detecting image tampering. This area of research is relatively new and only a few sources exist that directly relate to the detection of image forgeries. Fake images have become widespread in society today. The accessibility to powerful simple to use image editing computer software to end users helps make the job of manipulating image incredibly easy. One can find forged images used to sensationalize news, spread political propaganda and rumors, introduce psychological bias, etc. in all forms of media

    Statistical Tools for Digital Image Forensics

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    A digitally altered image, often leaving no visual clues of having been tampered with, can be indistinguishable from an authentic image. The tampering, however, may disturb some underlying statistical properties of the image. Under this assumption, we propose five techniques that quantify and detect statistical perturbations found in different forms of tampered images: (1) re-sampled images (e.g., scaled or rotated); (2) manipulated color filter array interpolated images; (3) double JPEG compressed images; (4) images with duplicated regions; and (5) images with inconsistent noise patterns. These techniques work in the absence of any embedded watermarks or signatures. For each technique we develop the theoretical foundation, show its effectiveness on credible forgeries, and analyze its sensitivity and robustness to simple counter-attacks

    Quality Classified Image Analysis with Application to Face Detection and Recognition

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    Motion blur, out of focus, insufficient spatial resolution, lossy compression and many other factors can all cause an image to have poor quality. However, image quality is a largely ignored issue in traditional pattern recognition literature. In this paper, we use face detection and recognition as case studies to show that image quality is an essential factor which will affect the performances of traditional algorithms. We demonstrated that it is not the image quality itself that is the most important, but rather the quality of the images in the training set should have similar quality as those in the testing set. To handle real-world application scenarios where images with different kinds and severities of degradation can be presented to the system, we have developed a quality classified image analysis framework to deal with images of mixed qualities adaptively. We use deep neural networks first to classify images based on their quality classes and then design a separate face detector and recognizer for images in each quality class. We will present experimental results to show that our quality classified framework can accurately classify images based on the type and severity of image degradations and can significantly boost the performances of state-of-the-art face detector and recognizer in dealing with image datasets containing mixed quality images.Comment: 6 page

    Additional information delivery to image content via improved unseen–visible watermarking

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    In a practical watermark scenario, watermarks are used to provide auxiliary information; in this way, an analogous digital approach called unseen–visible watermark has been introduced to deliver auxiliary information. In this algorithm, the embedding stage takes advantage of the visible and invisible watermarking to embed an owner logotype or barcodes as watermarks; in the exhibition stage, the equipped functions of the display devices are used to reveal the watermark to the naked eyes, eliminating any watermark exhibition algorithm. In this paper, a watermark complement strategy for unseen–visible watermarking is proposed to improve the embedding stage, reducing the histogram distortion and the visual degradation of the watermarked image. The presented algorithm exhibits the following contributions: first, the algorithm can be applied to any class of images with large smooth regions of low or high intensity; second, a watermark complement strategy is introduced to reduce the visual degradation and histogram distortion of the watermarked image; and third, an embedding error measurement is proposed. Evaluation results show that the proposed strategy has high performance in comparison with other algorithms, providing a high visual quality of the exhibited watermark and preserving its robustness in terms of readability and imperceptibility against geometric and processing attacks

    Active and passive approaches for image authentication

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    Ph.DDOCTOR OF PHILOSOPH

    Forensic Analysis of Digital Image Tampering

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    The use of digital photography has increased over the past few years, a trend which opens the door for new and creative ways to forge images. The manipulation of images through forgery influences the perception an observer has of the depicted scene, potentially resulting in ill consequences if created with malicious intentions. This poses a need to verify the authenticity of images originating from unknown sources in absence of any prior digital watermarking or authentication technique. This research explores the holes left by existing research; specifically, the ability to detect image forgeries created using multiple image sources and specialized methods tailored to the popular JPEG image format. In an effort to meet these goals, this thesis presents four methods to detect image tampering based on fundamental image attributes common to any forgery. These include discrepancies in 1) lighting and 2) brightness levels, 3) underlying edge inconsistencies, and 4) anomalies in JPEG compression blocks. Overall, these methods proved encouraging in detecting image forgeries with an observed accuracy of 60% in a completely blind experiment containing a mixture of 15 authentic and forged images
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