What question did this study set out to answer?

To develop SIDWA, a framework for detecting synthetic images using combined spatial and frequency analysis.

February 24, 2026Open Access

SIDWA: Synthetic Image Detection Based on Discrete Wavelet Transform Stem and Deformable Sliding Window Cross-Attention

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Authors

LLLuo Li TLTianyi Lu JSJiaxin Song

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Overview

Novel dual-branch detection method identifies synthetic images with advanced frequency and spatial techniques, indicating strong forensic capabilities.

Key Points

To develop SIDWA, a framework for detecting synthetic images using combined spatial and frequency analysis.
Proposed a dual-branch detection framework combining spatial and frequency domains.
Implemented a Deformable Sliding Window Cross-Attention module for dynamic edge capturing.
Utilized Discrete Wavelet Transform Stem to decompose images into multi-scale sub-bands.
Applied Frequency-Semantic Resonance Projector to guide anomaly detection.
Achieved average accuracy exceeding 95% on the SIDataset benchmark.
Demonstrated competitive inference time of 18.2 ms on NVIDIA A100 GPU.
Ablation studies confirmed the importance of learnable offsets and frequency integration for improved robustness.

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/699d3fd9de8e28729cf64a1d https://doi.org/10.3390/electronics15040891

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February 24, 2026Open Access

SIDWA: Synthetic Image Detection Based on Discrete Wavelet Transform Stem and Deformable Sliding Window Cross-Attention

View Full Paper

Ask AI

Bookmark

Authors

LLLuo Li TLTianyi Lu JSJiaxin Song

Discussion

Member takes

Overview

Novel dual-branch detection method identifies synthetic images with advanced frequency and spatial techniques, indicating strong forensic capabilities.

Key Points

To develop SIDWA, a framework for detecting synthetic images using combined spatial and frequency analysis.
Proposed a dual-branch detection framework combining spatial and frequency domains.
Implemented a Deformable Sliding Window Cross-Attention module for dynamic edge capturing.
Utilized Discrete Wavelet Transform Stem to decompose images into multi-scale sub-bands.
Applied Frequency-Semantic Resonance Projector to guide anomaly detection.
Achieved average accuracy exceeding 95% on the SIDataset benchmark.
Demonstrated competitive inference time of 18.2 ms on NVIDIA A100 GPU.
Ablation studies confirmed the importance of learnable offsets and frequency integration for improved robustness.

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/699d3fd9de8e28729cf64a1d https://doi.org/10.3390/electronics15040891

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