MDTL-NET: Computer-generated image detection based on multi-scale deep texture learning

doi:10.1016/j.eswa.2024.123368

MDTL-NET: Computer-generated image detection based on multi-scale deep texture learning

2024-08-01

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DOI：10.1016/j.eswa.2024.123368

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Distinguishing between computer -generated (CG) and natural photographic (PG) images is of great importance to verify the authenticity and originality of digital images. However, the recent cutting -edge generation methods enable high qualities of synthesis in CG images, which makes this challenging task even trickier. To address this issue, a novel multi -scale deep texture learning neural network coined as MDTL-NET is proposed for CG image detection. We first utilize a global texture representation module incorporating the ResNet architecture to capture multi -scale texture patterns. Then, a deep texture enhancement module based on a semantic segmentation map guided affine transformation operation is designed for texture difference amplification. To enhance performance, we equip the MDTL-NET with channel and spatial attention mechanisms, which refines intermediate features and facilitates trace exploration in different domains. Moreover, a Low -rank Tensor Representation (LTR) strategy is also used for feature fusion. Extensive experiments on three public datasets and a newly constructed dataset1 with more realistic and diverse images show that the proposed approach outperforms existing methods in the field by a clear margin. Besides, results also demonstrate the detection robustness and generalization ability of the proposed approach to postprocessing operations.

Keywords:

Computer-generated (CG)

Natural photographic (PG)

Image detection

Texture enhancement

Texture representation

MDTL-NET: Computer-generated image detection based on multi-scale deep texture learning

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