Name
Title | ||
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Flat and Shallow: Understanding Fake Image Detection Models by Architecture Profiling |
Abstract | ||
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ABSTRACT Digital image manipulations have been heavily abused to spread misinformation. Despite the great efforts dedicated in research community, prior works are mostly performance-driven, i.e., optimizing performances using standard/heavy networks designed for semantic classification. A thorough understanding for fake images detection models is still missing. This paper studies the essential ingredients for a good fake image detection model, by profiling the best-performing architectures. Specifically, we conduct a thorough analysis on a massive number of detection models, and observe how the performances are affected by different patterns of network structure. Our key findings include: 1) with the same computational budget, flat network structures (e.g., large kernel sizes, wide connections) perform better than commonly used deep networks; 2) operations in shallow layers deserve more computational capacities to trade-off performance and computational cost. These findings sketch a general profile for essential models of fake image detection, which show clear differences with those for semantic classification. Furthermore, based on our analysis, we propose a new Depth-Separable Search Space (DSS) for fake image detection. Compared to state-of-the-art methods, our model achieves competitive performance while saving more than 50% parameters. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3469877.3490566 | International Multimedia Conference |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |