AMSEANet: An Edge-Guided Adaptive Multi-Scale Network for Image Splicing Detection and Localization.

Area of Science:

• Computer Vision
• Digital Image Forensics
• Machine Learning

Background:

• Image splicing tamper detection faces challenges due to macroscopic semantic inconsistencies and microscopic artifacts.
• Conventional methods often isolate semantic understanding and artifact perception, limiting synergistic effectiveness.
• Frequency-domain information is crucial but often overlooked or poorly integrated, leading to feature conflicts.

Purpose of the Study:

• To propose a novel network, the Adaptive Multi-Scale Edge-Aware Network (AMSEANet), for enhanced image splicing tamper detection.
• To develop a frequency-aware process that synergistically combines semantic understanding and artifact perception.
• To effectively leverage frequency-domain information and preserve minute tampering clues.

Main Methods:

• Developed the Adaptive Multi-Scale Edge-Aware Network (AMSEANet) with a synergistic enhancement cascade architecture.
• Employed data-driven adaptive filters to focus on edge artifacts indicative of tampering.
• Utilized dense fusion and enhancement of cross-scale features to preserve fine details and tampering clues.

Main Results:

• The proposed AMSEANet achieved superior performance on multiple public datasets for image splicing tamper detection.
• Demonstrated excellent robustness against common post-processing attacks, including noise and JPEG compression.
• The frequency-aware approach effectively resolved feature conflicts and information redundancy.

Conclusions:

• AMSEANet offers a significant advancement in image splicing tamper detection by integrating semantic and artifact analysis.
• The network's ability to leverage frequency-domain information and handle edge artifacts enhances detection accuracy and robustness.
• This approach provides a more effective and synergistic solution for digital image forensics.