Forensic Analysis of Digital Image Tampering

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

Comparative Analysis of Image Enhancement Quality Based on Domains

First method is spatial domain and the effective of four diverse image spatial techniques (histogram equalization, adaptive histogram, histogram matching, and unsharp masking) produce sharpening and smoothening of image. Secondly, frequency domain technique and the effective of three diverse image spatial techniques (bilateral, homo-morphic and trilateral filter) were examined to achieve low noise image. Finally, SVD,QR,SLANT and HADAMARD was examined whichincreased human visual. For the above techniques, different quality parameters are evaluated. From the above evaluation, the proposed method identifies the best method among the three domains

Color image steganography in YCbCr space

Steganography is a best method for in secret communicating information during the transference of data. Images are an appropriate method that used in steganography can be used to protection the simple bits and pieces. Several systems, this one as color scale images steganography and grayscale images steganography, are used on color and store data in different techniques. These color images can have very big amounts of secret data, by using three main color modules. The different color modules, such as HSV-(hue, saturation, and value), RGB-(red, green, and blue), YCbCr-(luminance and chrominance), YUV, YIQ, etc. This paper uses unusual module to hide data: an adaptive procedure that can increase security ranks when hiding a top secret binary image in a RGB color image, which we implement the steganography in the YCbCr module space. We performed Exclusive-OR (XOR) procedures between the binary image and the RGB color image in the YCBCR module space. The converted byte stored in the 8-bit LSB is not the actual bytes; relatively, it is obtained by translation to another module space and applies the XOR procedure. This technique is practical to different groups of images. Moreover, we see that the adaptive technique ensures good results as the peak signal to noise ratio (PSNR) and stands for mean square error (MSE) are good. When the technique is compared with our previous works and other existing techniques, it is shown to be the best in both error and message capability. This technique is easy to model and simple to use and provides perfect security with unauthorized

Anti-Neuron Watermarking: Protecting Personal Data Against Unauthorized Neural Networks

We study protecting a user's data (images in this work) against a learner's unauthorized use in training neural networks. It is especially challenging when the user's data is only a tiny percentage of the learner's complete training set. We revisit the traditional watermarking under modern deep learning settings to tackle the challenge. We show that when a user watermarks images using a specialized linear color transformation, a neural network classifier will be imprinted with the signature so that a third-party arbitrator can verify the potentially unauthorized usage of the user data by inferring the watermark signature from the neural network. We also discuss what watermarking properties and signature spaces make the arbitrator's verification convincing. To our best knowledge, this work is the first to protect an individual user's data ownership from unauthorized use in training neural networks.Comment: Accepted to ECCV 202

PDF digital watermarking: Grayscale photocopy detection of printed documents

Digital watermarking is the technique used to provide additional and useful evidences for many application fields especially in copyright infringement detection.The focus of this paper is on Portable Document Format (PDF) watermarking and the printed watermarked copies.Digital watermarking is indeed suitable to detect unauthorized copies from any authentic sources. Colour theory and colour properties are studied on how to prevent yellow-watermarked stamps being photocopied in grayscale, particularly by using luminance concept on the documents.Please note that the technique applies only for grayscale photocopies, as the detection for coloured copies requires disparity techniques for the coloured detection

Review on tools for image detection forgery

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