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A Detection Method of Operated Fake-Images Using Robust Hashing.

Miki Tanaka¹, Sayaka Shiota¹, Hitoshi Kiya¹

¹Department of Computer Science, Tokyo Metropolitan University, 6-6 Asahigaoka, Tokyo 191-0065, Japan.

Journal of Imaging

|August 30, 2021

Summary

This summary is machine-generated.

This study introduces a novel robust hashing method for detecting fake or tampered images, even after compression and resizing. The technique offers improved accuracy compared to existing methods, especially when a reference image is available.

Keywords:

GAN fake images robust hashing synthetic media tamper detection

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Area of Science:

Computer Science
Digital Forensics
Image Processing

Background:

Social media platforms often alter uploaded images through recompression and resizing.
Conventional fake image detection methods lack robustness against these common image manipulations.

Purpose of the Study:

To develop a novel, robust method for detecting fake and tampered images.
To address the limitations of existing methods against image compression and resizing.

Main Methods:

Utilized a robust hashing method for fake/tampered image detection.
Employed hash values from both reference and query images for detection.
Introduced the use of original hash codes for enhanced comparison.

Main Results:

The proposed method demonstrates superior robustness in detecting fake images compared to conventional techniques.
Achieved state-of-the-art performance on various datasets, including those with GAN-generated fake images.
Successfully detected distortions caused by image compression and resizing.

Conclusions:

The novel robust hashing approach effectively detects fake images despite common manipulations.
This method offers a significant advancement for digital forensics and image authenticity verification.
Practical applications include monitoring synthetic images in commercial contexts.