Identification of Image Operations Based on Steganalytic Features

TL;DR

This paper proposes a universal detection method for various image operations by modeling them as steganography problems and applying steganalytic features, outperforming existing forensic techniques.

Contribution

It introduces a novel universal approach using steganalytic features to detect multiple image processing and anti-forensic operations, enhancing robustness and applicability.

Findings

Successfully detected 11 image processing operations.

Effectively identified 4 anti-forensic operations.

Outperformed existing forensic methods in experiments.

Abstract

Image forensics have attracted wide attention during the past decade. Though many forensic methods have been proposed to identify image forgeries, most of them are targeted ones, since their proposed features are highly dependent on the image operation under investigation. The performance of the well-designed features for detecting the targeted operation usually degrades significantly for other operations. On the other hand, a wise attacker can perform anti-forensics to fool the existing forensic methods, making countering anti-forensics become an urgent need. In this paper, we try to find a universal feature to detect various image processing and anti-forensic operations. Based on our extensive experiments and analysis, we find that any image processing/anti-forensic operations would inevitably modify many image pixels. This would change some inherent statistics within original images, which is similar to the case of steganography. Therefore, we model image processing/anti-forensic operations as steganography problems, and propose a detection strategy by applying steganalytic features. With some advanced steganalytic features, we are able to detect various image operations and further identify their types. In our experiments, we have tested several steganalytic features on 11 different kinds of typical image processing operations and 4 kinds of anti-forensic operations. The experimental results have shown that the proposed strategy significantly outperforms the existing forensic methods in both effectiveness and universality.