An Image Steganoganalyzer With Comprehensive Detection Performance

Effectively enhancing the weak stego signal while striking the balance among three evaluation metrics, i.e., detection accuracy, time cost, as well as the number of parameters (NP) is indeed a huge challenge for existing deep learning-based steganalysis detectors. In this letter, a novel steganalysis detector called GFS-Net is proposed, aiming at enhancing the stego signal as much as possible while balancing the three metrics to obtain comprehensive detection performance. In preprocessing, combining highly lightweight gated channel transformation with a pointwise convolution layer used for enlarging the number of channels enriches the expression of the stego signal by promoting cooperation among enlarged channels, thereby significantly improving the signal-to-noise ratio while avoiding occupying a large NP. Moreover, two FasterNet blocks equipped with partial convolution having a small NP, rather than residual blocks, are applied to the last two layers of feature extraction to efficiently extract the stego signal by reducing the calculation of similar features, so that the computational cost and NP are saved. Finally, compared to using the global average pooling (GAP) alone, the stylepooling jointly utilizing the global standard deviation pooling and GAP helps the subsequent fully connected better identify weak stego signal, and thus improves detection accuracy. By means of the above three perspectives, GFS-Net with only 0.13 M parameters is obtained. Experimental results also demonstrate that GFS-Net achieves higher detection accuracy and lower computational cost than state-of-the-art steganalysis detectors.