Open Access

ARTICLE

DDT-Net: Deep Detail Tracking Network for Image Tampering Detection

Jim Wong^1,2, Zhaoxiang Zang^3,*

1 Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang, 443002, China
2 College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, China
3 Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650504, China

* Corresponding Author: Zhaoxiang Zang. Email:

(This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)

Computers, Materials & Continua 2025, 83(2), 3451-3469. https://doi.org/10.32604/cmc.2025.061006

Received 14 November 2024; Accepted 03 March 2025; Issue published 16 April 2025

Abstract

In the field of image forensics, image tampering detection is a critical and challenging task. Traditional methods based on manually designed feature extraction typically focus on a specific type of tampering operation, which limits their effectiveness in complex scenarios involving multiple forms of tampering. Although deep learning-based methods offer the advantage of automatic feature learning, current approaches still require further improvements in terms of detection accuracy and computational efficiency. To address these challenges, this study applies the U-Net 3+ model to image tampering detection and proposes a hybrid framework, referred to as DDT-Net (Deep Detail Tracking Network), which integrates deep learning with traditional detection techniques. In contrast to traditional additive methods, this approach innovatively applies a multiplicative fusion technique during downsampling, effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise stream. This design enables noise residual features to guide the learning of semantic features more precisely and efficiently, thus facilitating comprehensive feature-level interaction. Furthermore, by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’ proficiency in detecting fine-grained local traces, the method significantly enhances the accuracy and robustness of tampered region detection. Compared with other approaches, the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k datasets. In addition, it has been extensively validated on other datasets, including CASIA and DIS25k. Experimental results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.

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