An object-based splicing forgery detection using multiple noise features

In our modern age, everything is accessible from anywhere to share thoughts and monuments with loved ones via social networking. On the other hand, different photo editing tools manipulate images and videos and allow an incredible opportunity to challenge the intended audience. When altered images go viral on social media, people may lose confidence, faith and integrity on the shared images. Thus necessitating a digital, trustworthy forensic technique to authenticate such images. This paper presents a novel feature extraction approach for detecting a tampered region. Individual objects are retrieved from the spliced image, and noise standard deviation is evaluated for each object in three different domains. The noise deviation features are then obtained based on pair-wise deviation using cosine similarity between individual objects. These features are fused using logistic regression to obtain a fake regression score that reveals the tampering region of a spliced image. The experimental findings suggest that the features and approach are superior and robust to state-of-the-art methods in detecting the tampered region.