To address the limited channel robustness of invertible neural networks against Gaussian noise, JPEG compression, and scaling/resampling in image information hiding, while maintaining their embedding capacity, we propose an invertible steganography algorithm that integrates a dual-branch attention mechanism in the frequency domain. First, a dual-branch attention module is designed to adaptively generate embedding weights from both global subband energy and local texture pixel dimensions after wavelet multi-frequency decomposition, thereby constructing a four-channel extended secret payload. Second, a four-channel extension strategy is developed to enhance embedding capacity without introducing random noise distortion. Additionally, dual enhancement mechanisms are employed to preprocess the original carrier before embedding, optimize frequency-domain feature distribution, and restore images contaminated by noise, compression, or scaling attacks during reconstruction. Experimental results demonstrate that under a 1:1.34 embedding ratio, the proposed algorithm achieves a clean-channel C-PSNR of 36.27 dB; under Gaussian noise (σ=10) and JPEG(QF=50) attacks, PSNR values for all channels reach 27.96 dB and 29.43 dB respectively. Compared to comparable models without attention mechanisms, this approach improves carrier quality and secret recovery metrics by over 2 dB on average, combining high transmission capacity with excellent channel robustness and visual imperceptibility.

Deceptive multimodal posts often remain globally plausible because the text and image can appear mutually consistent while the veracity claim depends on a small set of fragile evidence supports. Existing multimodal fake news detectors have advanced fusion, fine-grained interaction, knowledge-enhanced reasoning, and counterfactual learning, but they mainly strengthen the representation of the observed post and rarely measure which support actually sustains the final decision. We propose SRFP, a typed structural-response framework that represents each post as a configuration of entity, background, relation, and local-visual support representations. For each support type, SRFP applies a controlled cue-specific perturbation, compares the resulting factual and support-conditioned alternative states, encodes the state shift as a response trajectory, and compresses multi-type responses into a collapse signature for prediction and mechanism analysis. SRFP obtains F1/MCC scores of 94.15%/88.69% on MR2_Chinese, 93.33%/88.99% on MR2_English, and 92.68%/83.71% on Weibo. Ablation and matched-control experiments support the importance of preserving the semantic correspondence among support type, support representation, perturbation operator, and response descriptor, while progressive support-quality analysis characterizes the robustness of the learned response. These results suggest that multimodal fake news detection benefits from observing structural response under typed support challenges in addition to building stronger static image-text representations.