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Comparative Analysis (Extended)
Performance Against Existing Methods:
SobelMNet outperforms MedFCN and Streak Analysis in detecting median filtering, even under compressed settings.
Handles low-resolution images better than traditional deep learning models.
Faster inference time due to its lightweight CNN structure.
Robustness to Multiple Manipulations:
Detects median filtering even when combined with Gaussian Blur, Resampling, and Additive White Gaussian Noise (AWGN).
Achieves 100% accuracy when detecting median filtering + AWGN.
Maintains high accuracy across different levels of compression and scaling.
Computational Efficiency:
Uses fewer parameters than conventional CNN models.
Requires less training data compared to deeper architectures.
Can be implemented in real-time forensic applications.
Limitations:
Slightly lower accuracy when detecting median filtering + resampling due to structural distortions.
Performance may degrade with extreme compression (QF < 50).
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IEEE/ICACT20250214 Slide.16
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