UCF: Uncovering Common Features for Generalizable Deepfake Detection

Deepfake detection remains a challenging task due to the difficulty of generalizing to new types of forgeries. This problem primarily stems from the overfitting of existing detection methods to forgery-irrelevant features and method-specific patterns. The latter has been rarely studied and not well addressed by previous works. This paper presents a novel approach to address the two types of overfitting issues by uncovering common forgery features. Specifically, we first propose a disentanglement framework that decomposes image information into three distinct components: forgery-irrelevant, method-specific forgery, and common forgery features. To ensure the decoupling of method-specific and common forgery features, a multi-task learning strategy is employed, including a multi-class classification that predicts the category of the forgery method and a binary classification that distinguishes the real from the fake. Additionally, a conditional decoder is designed to utilize forgery features as a condition along with forgery-irrelevant features to generate reconstructed images. Furthermore, a contrastive regularization technique is proposed to encourage the disentanglement of the common and specific forgery features. Ultimately, we only utilize the common forgery features for the purpose of generalizable deepfake detection. Extensive evaluations demonstrate that our framework can perform superior generalization than current state-of-the-art methods

Study on the Effect of Cuisine Tourism Resource on Tourists’ Willingness to Visit

This article aims at examining if tourists’ evaluation of cuisine tourism resource has a positive effect on their willingness to visit (WTV) the destination (H1). In Study 1, the content analysis of travelogues of 60 Chinese major tourist cities shows that the scenic spots have a significant effect on WTV, while the effect of cuisine tourism resource on WTV is not supported. Moreover, the tourist city Chengdu with both abundant scenic spots and cuisine resources is chosen for further research of how cuisine resources influence tourist’ decisions. In term of 276 questionnaires (Study 2) and 30 interviewee (Study 3), the results show that the impact of the cuisine resource on WTV is moderated by the tourists’ evaluation on the scenic spots. Only when tourists have a high evaluation on scenic spots, the cuisine resource plays a positive impact on WTV, showing the auxiliary attraction of cuisine resource to tourists

Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimization

Backdoor defense, which aims to detect or mitigate the effect of malicious triggers introduced by attackers, is becoming increasingly critical for machine learning security and integrity. Fine-tuning based on benign data is a natural defense to erase the backdoor effect in a backdoored model. However, recent studies show that, given limited benign data, vanilla fine-tuning has poor defense performance. In this work, we provide a deep study of fine-tuning the backdoored model from the neuron perspective and find that backdoorrelated neurons fail to escape the local minimum in the fine-tuning process. Inspired by observing that the backdoorrelated neurons often have larger norms, we propose FTSAM, a novel backdoor defense paradigm that aims to shrink the norms of backdoor-related neurons by incorporating sharpness-aware minimization with fine-tuning. We demonstrate the effectiveness of our method on several benchmark datasets and network architectures, where it achieves state-of-the-art defense performance. Overall, our work provides a promising avenue for improving the robustness of machine learning models against backdoor attacks

Carb­oxy­methyl ursolate monohydrate

In the title compound, C28H50O5·H2O, all of the six-membered rings of the penta­cyclic triterpene skeleton adopt chair conformations. In the crystal, mol­ecules are linked by O—H⋯O and C—H⋯O hydrogen bonds

Identification of a novel conserved HLA-A*0201-restricted epitope from the spike protein of SARS-CoV

<p>Abstract</p> <p>Background</p> <p>The spike (S) protein is a major structural glycoprotein of coronavirus (CoV), the causal agent of severe acute respiratory syndrome (SARS). The S protein is a potent target for SARS-specific cell-mediated immune responses. However, the mechanism CoV pathogenesis in SARS and the role of special CTLs in virus clearance are still largely uncharacterized. Here, we describe a study that leads to the identification of a novel HLA-A*0201-restricted epitope from conserved regions of S protein.</p> <p>Results</p> <p>First, different SARS-CoV sequences were analyzed to predict eight candidate peptides from conserved regions of the S protein based upon HLA-A*0201 binding and proteosomal cleavage. Four of eight candidate peptides were tested by HLA-A*0201 binding assays. Among the four candidate peptides, Sp8 (S_958-966, VLNDILSRL) induced specific CTLs both <it>ex vivo </it>in PBLs of healthy HLA-A2⁺donors and in HLA-A2.1/K^btransgenic mice immunized with a plasmid encoding full-length S protein. The immunized mice released IFN-γ and lysed target cells upon stimulation with Sp8 peptide-pulsed autologous dendritic cells in comparison to other candidates.</p> <p>Conclusion</p> <p>These results suggest that Sp8 is a naturally processed epitope. We propose that Sp8 epitope should help in the characterization of mechanisms of virus control and immunopathology in SARS-CoV infection.</p