In this paper, we provide a unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors. The PRNU is obtained using a maximum-likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are then achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. We also include a benchmark implementation of this framework and detailed experimental validation. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising.

/pdf/determining-image-origin-and-integrity-using-sensor-noise-375kg5mshh.pdf

Determining Image Origin and Integrity Using Sensor Noise

With the advent of low-cost and high-resolution digital cameras, and sophisticated photo editing software, digital images can be easily manipulated and altered. Although good forgeries may leave no visual clues of having been tampered with, they may, nevertheless, alter the underlying statistics of an image. Most digital cameras, for example, employ a single sensor in conjunction with a color filter array (CFA), and then interpolate the missing color samples to obtain a three channel color image. This interpolation introduces specific correlations which are likely to be destroyed when tampering with an image. We quantify the specific correlations introduced by CFA interpolation, and describe how these correlations, or lack thereof, can be automatically detected in any portion of an image. We show the efficacy of this approach in revealing traces of digital tampering in lossless and lossy compressed color images interpolated with several different CFA algorithms.

Exposing digital forgeries in color filter array interpolated images

When creating a digital forgery, it is often necessary to combine several images, for example, when compositing one person's head onto another person's body. If these images were originally of different JPEG compression quality, then the digital composite may contain a trace of the original compression qualities. To this end, we describe a technique to detect whether the part of an image was initially compressed at a lower quality than the rest of the image. This approach is applicable to images of high and low quality as well as resolution.

Exposing Digital Forgeries From JPEG Ghosts

The aim of this survey is to provide a comprehensive overview of the state of the art in the area of image forensics. These techniques have been designed to identify the source of a digital image or to determine whether the content is authentic or modified, without the knowledge of any prior information about the image under analysis (and thus are defined as passive). All these tools work by detecting the presence, the absence, or the incongruence of some traces intrinsically tied to the digital image by the acquisition device and by any other operation after its creation. The paper has been organized by classifying the tools according to the position in the history of the digital image in which the relative footprint is left: acquisition-based methods, coding-based methods, and editing-based schemes.

/pdf/an-overview-on-image-forensics-12zmktrxdo.pdf

An Overview on Image Forensics

http://www.dxsx.net/admin/editor/upload/201412/2014122915411949.pdf

Digital image forgery detection using passive techniques: A survey

In this paper we present a framework for digital image forensics. Based on the assumptions that some processing operations must be done on the image before it is doctored and an expected measurable distortion after processing an image, we design classifiers that discriminates between original and processed images. We propose a novel way of measuring the distortion between two images, one being the original and the other processed. The measurements are used as features in classifier design. Using these classifiers we test whether a suspicious part of a given image has been processed with a particular method or not. Experimental results show that with a high accuracy we are able to tell if some part of an image has undergone a particular or a combination of processing methods.

/pdf/a-classifier-design-for-detecting-image-manipulations-1xmgarto5z.pdf

N. Memon

Papers

A classifier design for detecting image manipulations