Mining Fine-Grain Face Forgery Cues with Fusion Modality

Face forgery detection is drawing ever-increasing attention in the academic community owing to security concerns.Despite the considerable progress in existing methods,we note that:Previous works overlooked finegrain forgery cues with high transferability.Such cues positively impact the model’s accuracy and generalizability.Moreover,single-modality often causes overfitting of the model,and Red-Green-Blue(RGB)modal-only is not conducive to extracting the more detailed forgery traces.We propose a novel framework for fine-grain forgery cues mining with fusion modality to cope with these issues.First,we propose two functional modules to reveal and locate the deeper forged features.Our method locates deeper forgery cues through a dual-modality progressive fusion module and a noise adaptive enhancement module,which can excavate the association between dualmodal space and channels and enhance the learning of subtle noise features.A sensitive patch branch is introduced on this foundation to enhance the mining of subtle forgery traces under fusion modality.The experimental results demonstrate that our proposed framework can desirably explore the differences between authentic and forged images with supervised learning.Comprehensive evaluations of several mainstream datasets show that our method outperforms the state-of-the-art detection methods with remarkable detection ability and generalizability.

Deepfake Video Detection Based on Improved CapsNet and Temporal–Spatial Features

Rapid development of deepfake technology led to the spread of forged audios and videos across network platforms,presenting risks for numerous countries,societies,and individuals,and posing a serious threat to cyberspace security.To address the problem of insufficient extraction of spatial features and the fact that temporal features are not considered in the deepfake video detection,we propose a detection method based on improved CapsNet and temporal–spatial features(iCapsNet–TSF).First,the dynamic routing algorithm of CapsNet is improved using weight initialization and updating.Then,the optical flow algorithm is used to extract interframe temporal features of the videos to form a dataset of temporal–spatial features.Finally,the iCapsNet model is employed to fully learn the temporal–spatial features of facial videos,and the results are fused.Experimental results show that the detection accuracy of iCapsNet–TSF reaches 94.07%,98.83%,and 98.50%on the Celeb-DF,FaceSwap,and Deepfakes datasets,respectively,displaying a better performance than most existing mainstream algorithms.The iCapsNet–TSF method combines the capsule network and the optical flow algorithm,providing a novel strategy for the deepfake detection,which is of great significance to the prevention of deepfake attacks and the preservation of cyberspace security.

Multi-Branch Deepfake Detection Algorithm Based on Fine-Grained Features

With the rapid development of deepfake technology,the authenticity of various types of fake synthetic content is increasing rapidly,which brings potential security threats to people’s daily life and social stability.Currently,most algorithms define deepfake detection as a binary classification problem,i.e.,global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or false.However,the differences between real and fake samples are often subtle and local,and such global feature-based detection algorithms are not optimal in efficiency and accuracy.To this end,to enhance the extraction of forgery details in deep forgery samples,we propose a multi-branch deepfake detection algorithm based on fine-grained features from the perspective of fine-grained classification.First,to address the critical problem in locating discriminative feature regions in fine-grained classification tasks,we investigate a method for locating multiple different discriminative regions and design a lightweight feature localization module to obtain crucial feature representations by augmenting the most significant parts of the feature map.Second,using information complementation,we introduce a correlation-guided fusion module to enhance the discriminative feature information of different branches.Finally,we use the global attention module in the multi-branch model to improve the cross-dimensional interaction of spatial domain and channel domain information and increase the weights of crucial feature regions and feature channels.We conduct sufficient ablation experiments and comparative experiments.The experimental results show that the algorithm outperforms the detection accuracy and effectiveness on the FaceForensics++and Celeb-DF-v2 datasets compared with the representative detection algorithms in recent years,which can achieve better detection results.

Hydrolysis of cellobiose catalyzed by zeolites—the role of acidity and micropore structure

The roles of acidity and micropore structure of zeolite were studied in the hydrolysis of the model oligosaccharide of cellulose–cellobiose. HZSM-5, HY, HMOR and Hβ zeolites were selected as model catalysts for the hydrolysis of cellobiose. The effect of acidity of zeolite, including the strength, type and location, on its catalytic activity was investigated. The strong Br？nsted acid sites located in micropores are the active sites for the hydrolysis of cellobiose to glucose. Meanwhile, the catalytic performance of zeolite is also dependent on the micropore size of zeolite.

Studies on the roles of vanadyl sulfate and sodium nitrite in catalytic oxidation of benzyl alcohol with molecular oxygen

An efficient catalytic system consisting of vanadyl sulfate/sodium nitrite was disclosed previously for the oxidation of benzylic alcohols into aldehydes with molecular oxygen.However,the roles of catalyst components were not investigated.In this paper,we examined catalytic oxidation of benzyl alcohol as a model reaction,especially by infrared spectroscopy.The role of each component is discussed including nitrite,vanadyl,sulphate,and water.Sodium nitrite could be converted into nitrate and nitric acid.The vanadium(IV)could be smoothly oxidized into vanadium(V)under mild and acidic conditions without any organic ligands.The transformation of sulfate and bisulfate,the cessation of an induction period,and the oxidation of benzyl alcohol were closely interrelated.The multiple roles of water are discussed,including reduction of the induction period,participation in redox cycles of nitric compounds,deactivation of vanadium,and as a byproduct of oxidation.This study contributes to further development of aerobic oxidation using vanadium based catalysts.

Synthesis of Mesoporous Anatase TiO2 Sphere with High Surface Area and Enhanced Photocatalytic Activity

Mesoporous anatase TiO2 spheres with high surface area（119 m^2g^（-1）） were successfully synthesized via a facile and green template-free method. The prepared TiO2 was characterized by X-ray diffraction（XRD）,N2 adsorption, scanning electron microscopy（SEM）, transmission electron microscopy（TEM） and UV–vis absorbance spectra. It was found that the prepared TiO2 is characterized by pure anatase phase, which shows uniform spheres and has a typical mesostructure with a high specific surface area and a large pore volume. The effects of complexant（acetylacetone） amount, crystallization temperature and calcination temperature were also investigated. Based on the results, a sketch for the preparation of mesoporous TiO2 was proposed. First, complex formed between tetrabutyl titanate and acetylacetone in ethanol. After introduction of aqueous of ammonia sulfate and urea, hydrolysis of tetrabutyl titanate would occur slowly,and sol of TiO2 was formed. Then, crystallization proceeded under hydrothermal conditions. Calcination process favored the formation of bigger TiO2 crystal through combining of the small crystals in TiO2.This led to the formation of bigger mesopores between TiO2 crystals. Photocatalytic activity of the prepared TiO2 was evaluated by decomposition of methyl orange.