Digital Image Tamper Detection Technique Based on Spectrum Analysis of CFA Artifacts

Area of Science:

• Digital Forensics
• Image Processing
• Computer Vision

Background:

• Mobile devices with advanced cameras and editing apps facilitate malicious digital image alteration.
• Existing tamper detection methods may not cover a wide range of manipulations.

Purpose of the Study:

• To develop a new digital image tamper detection technique.
• To leverage Color Filter Array (CFA) artifacts for detecting image manipulations.
• To achieve high accuracy in identifying tampered image regions.

Main Methods:

• The method is based on detecting inconsistencies in Color Filter Array (CFA) artifacts.
• It computes the probability of each pixel being interpolated.
• Discrete Cosine Transform (DCT) is applied to probability maps to identify tampered areas.

Main Results:

• The proposed method effectively detects various manipulations including copy-move, resizing, rotation, filtering, and colorization.
• Affected zones in tampered images are clearly highlighted.
• An accuracy of 86% was achieved on a publicly available dataset.

Conclusions:

• The developed technique reliably detects digital image tampering based on CFA inconsistencies.
• The method demonstrates effectiveness across a broad spectrum of image manipulations.
• It offers a robust, training-free approach to digital image forensics.