Chest X-ray report generation aims to reduce radiologists' workload by automatically producing high-quality preliminary reports. A critical yet underexplored aspect of this task is the effective use of patient-specific prior knowledge---including clinical context (e.g., symptoms, medical history) and the most recent prior image---which radiologists routinely rely on for diagnostic reasoning. Most existing methods generate reports from single images, neglecting this essential prior information and thus failing to capture diagnostic intent or disease progression. To bridge this gap, we propose PriorRG, a novel chest X-ray report generation framework that emulates real-world clinical workflows via a two-stage training pipeline. In Stage 1, we introduce a prior-guided contrastive pre-training scheme that leverages clinical context to guide spatiotemporal feature extraction, allowing the model to align more closely with the intrinsic spatiotemporal semantics in radiology reports. In Stage 2, we present a prior-aware coarse-to-fine decoding for report generation that progressively integrates patient-specific prior knowledge with the vision encoder's hidden states. This decoding allows the model to align with diagnostic focus and track disease progression, thereby enhancing the clinical accuracy and fluency of the generated reports. Extensive experiments on MIMIC-CXR and MIMIC-ABN datasets demonstrate that PriorRG outperforms state-of-the-art methods, achieving a 3.6% BLEU-4 and 3.8% F1 score improvement on MIMIC-CXR, and a 5.9% BLEU-1 gain on MIMIC-ABN.

As accurate face recognition (FR) models based on deep learning can be easily trained using face images from various social media platforms, this phenomenon has raised ever-increasing concerns regarding user privacy. To address this issue, we investigate a privacy protection scheme based on multifeature fusion tensor (PPS-MFFT). Different from previous studies using a single feature or simple combination of several features, for every face image, PPS-MFFT first constructs a multifeature fusion tensor through hierarchically exploiting the correlations and complementarity between deep-learning features and those handcrafted features for stronger robustness and transferability. Further, on the basis of such tensors, the target images are reasonably chosen to enhance the camouflage effects while maintaining the visual similarities for final perturbed images, which are generated by means of developing a new optimization model for better tradeoff between effectiveness and practicability. Finally, the measurement results validate that both higher protective efficacy (e.g., 16% more in misidentifying the original face images) and acceptable visual effects can be obtained by PPS-MFFT when compared to the existing methods, and thus demonstrate the generality and applicability of our scheme.