Image forensics based on reverse engineering.

摘要

Today with the advent of low-cost imaging devices, such as smart phones, digital.;cameras and surveillance video systems, digital images become quite common in our.;everyday life. People tend to believe the scene they have seen, even if the scene is.;presented in the form of an digital image, as a proverb says, 'Seeing is believing'.;However are those images really trustworthy as people have thought? In this mul-.;timedia world, with the wide-spread availability of those sophisticated image-editing.;software, such as PhotoShop and Gimp, it is easy for people to modify images to.;hide some information or to add a non-existing scene. These manipulations usually.;leave no visual clues in the tampered image. As a result, the above proverb no longer.;holds.;To address this problem, `digital image forensics' was developed. Digital image.;forensics aims to verify the authentication and integrity of a digital image, without the.;knowledge of any prior information about the questioned image. It mainly includes.;two tasks: to determine whether an image is authentic and to identify the source.;camera of an image.;What distinguishes the original image from the manipulated image is the acqui-.;sition process inside the digital camera, which should naturally be the only reliable.;solution to conquer this problem. In this work, we analyze some key operations along.;the image acquisition pipeline, and use the cracked information to perform forensic.;tasks. The contributions can be grouped into three categories: white balance(WB) ,;color demosaicking and defocus aberration blurs.;The thesis starts with exposing which white balance algorithm has been applied.;in the imaging pipeline. The theoretical basis lies on the fact that, given an image,;applying the same white balance operation again would not change the image. With.;the proposed approach, the average accuracy of source camera identification is 99.3%.;for 5 cameras of different brands, 98.6% for 17 cameras of different models, and 98.5%.;for 15 cameras equally from 3 models. This is the first time white balance has been.;used in source camera identification, and it leads to an almost perfect result.;Most commercial cameras have only one CCD/CMOS sensor, which produces.;just a gray scale image. In order to get a colored one, cameras apply a process called.;demosaicking. This thesis estimates the model and parameters of the demosaicking.;process to detect forgery. With this method, we can identify which part of the image.;that is inconsistent with the rest, in the form of their corresponding estimation error.;This is the first time that the copy-move area from another image can be exposed.;using demosaicking.;The third part of this thesis aims at integrity verification using image defocus blur.;We can calculate the image defocus aberration, and estimate its depth information.;Also from defocus aberration consistency, we can determine whether an image has.;been altered. This is the first time defocus blur has been used to perform forensic.;task. The proposed method increases the average accuracy of splicing detection to.;81%, while the best existing published result using the same database is only 68.8%.