HFORD: High-Fidelity and Occlusion-Robust De-identification for Face Privacy Protection

With the popularity of smart devices and the development of computer vision technology, concerns about face privacy protection are growing. The face de-identification technique is a practical way to solve the identity protection problem. The existing facial de-identification methods have revealed several problems, including the impact on the realism of anonymized results when faced with occlusions and the inability to maintain identity-irrelevant details in anonymized results. We present a High-Fidelity and Occlusion-Robust De-identification (HFORD) method to deal with these issues. This approach can disentangle identities and attributes while preserving image-specific details such as background, facial features (e.g., wrinkles), and lighting, even in occluded scenes. To disentangle the latent codes in the GAN inversion space, we introduce an Identity Disentanglement Module (IDM). This module selects the latent codes that are closely related to the identity. It further separates the latent codes into identity-related codes and attribute-related codes, enabling the network to preserve attributes while only modifying the identity. To ensure the preservation of image details and enhance the network's robustness to occlusions, we propose an Attribute Retention Module (ARM). This module adaptively preserves identity-irrelevant details and facial occlusions and blends them into the generated results in a modulated manner. Extensive experiments show that our method has higher quality, better detail fidelity, and stronger occlusion robustness than other face de-identification methods

Diff-Privacy: Diffusion-based Face Privacy Protection

Privacy protection has become a top priority as the proliferation of AI techniques has led to widespread collection and misuse of personal data. Anonymization and visual identity information hiding are two important facial privacy protection tasks that aim to remove identification characteristics from facial images at the human perception level. However, they have a significant difference in that the former aims to prevent the machine from recognizing correctly, while the latter needs to ensure the accuracy of machine recognition. Therefore, it is difficult to train a model to complete these two tasks simultaneously. In this paper, we unify the task of anonymization and visual identity information hiding and propose a novel face privacy protection method based on diffusion models, dubbed Diff-Privacy. Specifically, we train our proposed multi-scale image inversion module (MSI) to obtain a set of SDM format conditional embeddings of the original image. Based on the conditional embeddings, we design corresponding embedding scheduling strategies and construct different energy functions during the denoising process to achieve anonymization and visual identity information hiding. Extensive experiments have been conducted to validate the effectiveness of our proposed framework in protecting facial privacy.Comment: 17page

CatVersion: Concatenating Embeddings for Diffusion-Based Text-to-Image Personalization

We propose CatVersion, an inversion-based method that learns the personalized concept through a handful of examples. Subsequently, users can utilize text prompts to generate images that embody the personalized concept, thereby achieving text-to-image personalization. In contrast to existing approaches that emphasize word embedding learning or parameter fine-tuning for the diffusion model, which potentially causes concept dilution or overfitting, our method concatenates embeddings on the feature-dense space of the text encoder in the diffusion model to learn the gap between the personalized concept and its base class, aiming to maximize the preservation of prior knowledge in diffusion models while restoring the personalized concepts. To this end, we first dissect the text encoder's integration in the image generation process to identify the feature-dense space of the encoder. Afterward, we concatenate embeddings on the Keys and Values in this space to learn the gap between the personalized concept and its base class. In this way, the concatenated embeddings ultimately manifest as a residual on the original attention output. To more accurately and unbiasedly quantify the results of personalized image generation, we improve the CLIP image alignment score based on masks. Qualitatively and quantitatively, CatVersion helps to restore personalization concepts more faithfully and enables more robust editing.Comment: For the project page, please visit https://royzhao926.github.io/CatVersion-page

All-to-key Attention for Arbitrary Style Transfer

Attention-based arbitrary style transfer studies have shown promising performance in synthesizing vivid local style details. They typically use the all-to-all attention mechanism -- each position of content features is fully matched to all positions of style features. However, all-to-all attention tends to generate distorted style patterns and has quadratic complexity, limiting the effectiveness and efficiency of arbitrary style transfer. In this paper, we propose a novel all-to-key attention mechanism -- each position of content features is matched to stable key positions of style features -- that is more in line with the characteristics of style transfer. Specifically, it integrates two newly proposed attention forms: distributed and progressive attention. Distributed attention assigns attention to key style representations that depict the style distribution of local regions; Progressive attention pays attention from coarse-grained regions to fine-grained key positions. The resultant module, dubbed StyA2K, shows extraordinary performance in preserving the semantic structure and rendering consistent style patterns. Qualitative and quantitative comparisons with state-of-the-art methods demonstrate the superior performance of our approach

Ultrafast Spin-To-Charge Conversion at the Surface of Topological Insulator Thin Films

Strong spin-orbit coupling, resulting in the formation of spin-momentum-locked surface states, endows topological insulators with superior spin-to-charge conversion characteristics, though the dynamics that govern it have remained elusive. Here, we present an all-optical method that enables unprecedented tracking of the ultrafast dynamics of spin-to-charge conversion in a prototypical topological insulator Bi$_2$Se$_3$/ferromagnetic Co heterostructure, down to the sub-picosecond timescale. Compared to pure Bi$_2$Se$_3$ or Co, we observe a giant terahertz emission in the heterostructure than originates from spin-to-charge conversion, in which the topological surface states play a crucial role. We identify a 0.12-picosecond timescale that sets a technological speed limit of spin-to-charge conversion processes in topological insulators. In addition, we show that the spin-to-charge conversion efficiency is temperature independent in Bi$_2$Se$_3$ as expected from the nature of the surface states, paving the way for designing next-generation high-speed opto-spintronic devices based on topological insulators at room temperature.Comment: 19 pages, 4 figure

Plasma Oxidation of H2S over Non-stoichiometric LaxMnO3 Perovskite Catalysts in a Dielectric Barrier Discharge Reactor

In this work, plasma-catalytic removal of H2S over LaxMnO3 (x = 0.90, 0.95, 1, 1.05 and 1.10) has been studied in a coaxial dielectric barrier discharge (DBD) reactor. The non-stoichiometric effect of the LaxMnO3 catalysts on the removal of H2S and sulfur balance in the plasma-catalytic process has been investigated as a function of specific energy density (SED). The integration of the plasma with the LaxMnO3 catalysts significantly enhanced the reaction performance compared to the process using plasma alone. The highest H2S removal of 96.4% and sulfur balance of 90.5% were achieved over the La0.90MnO3 catalyst, while the major products included SO2 and SO3. The missing sulfur could be ascribed to the sulfur deposited on the catalyst surfaces. The non-stoichiometric LaxMnO3 catalyst exhibited larger specific surface areas and smaller crystallite sizes compared to the LaMnO3 catalyst. The non-stoichiometric effect changed their redox properties as the decreased La/Mn ratio favored the transformation of Mn3+ to Mn4+, which contributed to the generation of oxygen vacancies on the catalyst surfaces. The XPS and H2-TPR results confirmed that the Mn-rich catalysts showed the higher relative concentration of surface adsorbed oxygen (Oads) and lower reduction temperature compared to LaMnO3 catalyst. The reaction performance of the plasma-catalytic oxidation of H2S is closely related to the relative concentration of Oads formed on the catalyst surfaces and the reducibility of the catalysts

Alternative splicing event associated with immunological features in bladder cancer

Bladder cancer (BLCA) is the most prevalent urinary tumor with few treatments. Alternative splicing (AS) is closely related to tumor development and tumor immune microenvironment. However, the comprehensive analysis of AS and prognosis and immunological features in BLCA is still lacking. In this study, we downloaded RNA-Seq data and clinical information from The Cancer Genome Atlas (TCGA) database, and AS events were acquired from the TCGA Splice-seq. A total of eight prognostic AS events (C19orf57|47943|ES, ANK3|11845|AP, AK9|77203|AT, GRIK2|77096|AT, DYM|45472|ES, PTGER3|3415|AT, ACTG1|44120|RI, and TRMU|62711|AA) were identified by univariate analysis and least absolute shrinkage and selection operator (LASSO) regression analysis to construct a risk score model. The Kaplan–Meier analysis revealed that the high-risk group had a worse prognosis compared with the low-risk group. The area under the receiver operating characteristic (ROC) curves (AUCs) for this risk score model in 1, 3, and 5 years were 0.698, 0.742, and 0.772, respectively. One of the prognostic AS event-related genes, TRMU, was differentially expressed between tumor and normal tissues in BLCA. The single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithm showed that both the risk score model and TRMU were significantly associated with tumor immune microenvironment and immune status (immune cells, immune-related pathway, and immune checkpoint) in BLCA patients. The TIMER database confirmed the relationship between the expression of TRMU and immune cells and checkpoint genes. Furthermore, Cytoscape software 3.8.0 was used to construct the regulatory network between AS and splicing factors (SFs). Our study demonstrated that AS events were powerful biomarkers to predict the prognosis and immune status in BLCA, which may be potential therapeutic targets in BLCA