Adversarial attack methods have achieved satisfactory results in white-box attack scenarios, but their performance declines when transferred to other deep neural network (DNN) models. Currently, there are many methods to improve the transferability of adversarial samples, and enhancing transferability through input transformations is an effective approach. However, most existing input transformations are performed in the spatial domain, neglecting transformations in the frequency domain. Therefore, this paper proposes a novel input transformation-based attack: the frequency domain enhancement (FDE) method, which performs input transformations in the frequency domain to increase input diversity. Specifically, this method processes input images in the frequency domain, suppresses high-frequency information in the input images, and then randomly amplifies certain frequency domain information, generating adversarial samples with stronger transferability. Experimental results show that adversarial samples generated through FDE demonstrate significant improvement in transferability on both undefended and defended models on the ImageNet dataset. Notably, this method can be combined with many existing techniques to further enhance the transferability of adversarial samples.

Reversible data hiding (RDH) in structured images remains challenging: most deep-learning methods target natural images, where distortions can be spread across the entire picture. When the carrier contains functionally critical regions—the finder and alignment patterns of a QR code, for instance—a uniform allocation of distortion directly hurts machine readability and, as a result, reversibility. This paper presents AR-WeNet, a structure-aware reversible data hiding framework for images with rigid functional structures. It brings together two components: (1) a Regulatory Information Encryption Module (RIEM) that turns a structured regulatory record into an encrypted binary pattern through UTF-8 encoding, QR error-correction coding, and Arnold scrambling; and (2) a DWT-domain invertible neural network backbone enhanced by Multi-Scale Wavelet Subband Attention (MSWA). A region-adaptive embedding (RAE) strategy, steered by a predefined structural weight map, concentrates embedding distortions in redundant data modules while leaving functional modules intact. On the self-constructed E-Voucher-500 benchmark, AR-WeNet reaches 63.29 dB PSNR, 0.9999 SSIM, a 0.000% pre-correction bit error rate, and 100% exact QR decoding success—an 8.00 dB improvement over the uniform-weighting WeNet baseline. Robustness tests under JPEG compression, Gaussian noise, and cropping confirm resilience at both the bit level and the decoding level. On DIV2K, the INN backbone achieves 55.27 dB, surpassing HiNet by 10.41 dB, showing that the design extends beyond structured images to natural-image hiding.