High-Quality Full-Size Image Steganography Method Based on Improved U-Net and Hybrid Attention Mechanism
Image steganography is a technique for hiding secret information within images to prevent detection.Current image steganography models face issues,such as poor image generation quality and weak resistance to steganalysis.The hybrid attention mechanism can suppress meaningless channel information to avoid artifacts in the stego image and assign different weights based on the importance of different positions in the cover image,thereby finding more suitable hiding areas for the secret information.Based on these features,this paper proposed a high-quality,full-size image steganography method based on U-Net and hybrid attention mechanisms.The model comprised three subnetworks:an encoder,an extractor,and a discriminator.The encoder was designed using an improved U-Net structure and a hybrid attention mechanism module;the extractor was designed using convolutional neural networks and a hybrid attention mechanism module;the discriminator enhanced the model's security.Experimental results show that this method can completely hide a 256×256 color secret image within a cover image of the same size,achieving high-quality image steganography without reducing the steganographic capacity.This method demonstrates good visual quality and hiding capacity on the ImageNet,COCO,and DIV2K datasets.On the ImageNet dataset,the PSNR values can reach up to 40.143 dB(cover image and stego image)and 42.082 dB(secret image and reconstructed secret image),while also improving the model's resistance to steganalysis.
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