PENet+: A Lightweight Residual Transformer Framework for Efficient Image Steganalysis

Image steganalysis, the detection of hidden information embedded in digital images, is a core component of modern cybersecurity and digital forensics. Recent residual Transformer architectures, such as Pixel-Difference-Convolution and Enhanced-Transformer-Network (PENet) [1], achieve strong detection accuracy, but their computational and memory demands hinder deployment in real-time or resource-constrained settings. In this paper, we present PENet+, a lightweight steganalysis framework that preserves PENet’s discriminative structure while substantially improving efficiency. Instead of redesigning or compressing the attention blocks, we retain PENet’s self-attention topology for reproducibility and introduce a classifier-streamlining stage that progressively narrows the SPP→FC1 input channels, yielding large reductions in parameters and FLOPs with negligible accuracy loss. Here, SPP denotes spatial pyramid pooling, FC1 denotes the first fully connected layer after SPP, and HPF denotes high pass filter. We further refine the HPF stem with an activation-aware selection mechanism that aggregates high-pass filter responses in an early stage and selects a balanced SRM–Gabor top-Ksubset, and we replace PENet’s backbone with a MobileNetV2-style inverted residual network for efficient spatial processing. A balanced HPF configuration withK=31 filters (16 Gabor + 15 SRM) matches or surpasses the accuracy of heavier configurations while lowering compute. Finally, we revisit activation design and motivate PReLU from a steganalysis standpoint, arguing that preserving negative responses is beneficial for capturing weak stego cues that ReLU would otherwise suppress. On the ALASKA2 [2] QF90 color benchmark at 512×512 resolution, PENet+ achieves up to a 45.5% reduction in parameters and approximately 97% fewer FLOPs while maintaining accuracy comparable to PENet. These results indicate that the proposed architecture offers a practical potential toward real-time, on-device steganalysis.