The widespread availability of digital multimedia data has led to a new challenge in digital forensics.Traditional source camera identification algorithms usually rely on various traces in the capturing process.Howeve...The widespread availability of digital multimedia data has led to a new challenge in digital forensics.Traditional source camera identification algorithms usually rely on various traces in the capturing process.However,these traces have become increasingly difficult to extract due to wide availability of various image processing algorithms.Convolutional Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera devices.However,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall differences.In this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these issues.The proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature representation.This representation is then fed into a subsequent camera fingerprint classification network.Building upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone network.Furthermore,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.展开更多
It is well known that a block discrete cosine transform compressed image exhibits visually annoying blocking artifacts at low-bit-rate. A new post-processing deblocking algorithm in wavelet domain is proposed. The alg...It is well known that a block discrete cosine transform compressed image exhibits visually annoying blocking artifacts at low-bit-rate. A new post-processing deblocking algorithm in wavelet domain is proposed. The algorithm exploits blocking-artifact features shown in wavelet domain. The energy of blocking artifacts is concentrated into some lines to form annoying visual effects after wavelet transform. The aim of reducing blocking artifacts is to capture excessive energy on the block boundary effectively and reduce it below the visual scope. Adaptive operators for different subbands are computed based on the wavelet coefficients. The operators are made adaptive to different images and characteristics of blocking artifacts. Experimental results show that the proposed method can significantly improve the visual quality and also increase the peak signal-noise-ratio(PSNR) in the output image.展开更多
12-pulse rectifier is extensively used in high power rectification, and the delta-connected autotransformer and wye-connected autotransformer are its two most popular phase-shift transformers. This paper compares the ...12-pulse rectifier is extensively used in high power rectification, and the delta-connected autotransformer and wye-connected autotransformer are its two most popular phase-shift transformers. This paper compares the 12-pulse rectifiers using the two transformers via calculating the input line current, load voltage, kVA ratings of the two autotransformer, kVA ratings of the auxiliary magnetic devices. From the viewpoint of power quality of AC mains and DC side, the two 12-pulse rectifiers are the same. The kVA rating of the IPR in the two 12-pulse rectifiers are equal, and the kVA rating of the ZSBT in the two 12-pulse rectifier are also equal to each other, under the same load power. However, the kVA of the deltaconnected autotransformer is less than that of the wyeconnected autotransformer under the same load power.Some experimental results are shown to validate the correctness of the theoretical analysis.展开更多
基金This work was funded by the National Natural Science Foundation of China(Grant No.62172132)Public Welfare Technology Research Project of Zhejiang Province(Grant No.LGF21F020014)the Opening Project of Key Laboratory of Public Security Information Application Based on Big-Data Architecture,Ministry of Public Security of Zhejiang Police College(Grant No.2021DSJSYS002).
文摘The widespread availability of digital multimedia data has led to a new challenge in digital forensics.Traditional source camera identification algorithms usually rely on various traces in the capturing process.However,these traces have become increasingly difficult to extract due to wide availability of various image processing algorithms.Convolutional Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera devices.However,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall differences.In this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these issues.The proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature representation.This representation is then fed into a subsequent camera fingerprint classification network.Building upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone network.Furthermore,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.
基金Science and Technology Project of Guangdong Province(2006A10201003)2005 Startup Project of Jinan University(51205067)Soft Science Project of Guangdong Province(2006B70103011)
文摘It is well known that a block discrete cosine transform compressed image exhibits visually annoying blocking artifacts at low-bit-rate. A new post-processing deblocking algorithm in wavelet domain is proposed. The algorithm exploits blocking-artifact features shown in wavelet domain. The energy of blocking artifacts is concentrated into some lines to form annoying visual effects after wavelet transform. The aim of reducing blocking artifacts is to capture excessive energy on the block boundary effectively and reduce it below the visual scope. Adaptive operators for different subbands are computed based on the wavelet coefficients. The operators are made adaptive to different images and characteristics of blocking artifacts. Experimental results show that the proposed method can significantly improve the visual quality and also increase the peak signal-noise-ratio(PSNR) in the output image.
基金supported by National Natural Science Foundation of China(No.51307034)in part by the Natural Science Foundation of Shandong Province(No.ZR2013EEQ002)
文摘12-pulse rectifier is extensively used in high power rectification, and the delta-connected autotransformer and wye-connected autotransformer are its two most popular phase-shift transformers. This paper compares the 12-pulse rectifiers using the two transformers via calculating the input line current, load voltage, kVA ratings of the two autotransformer, kVA ratings of the auxiliary magnetic devices. From the viewpoint of power quality of AC mains and DC side, the two 12-pulse rectifiers are the same. The kVA rating of the IPR in the two 12-pulse rectifiers are equal, and the kVA rating of the ZSBT in the two 12-pulse rectifier are also equal to each other, under the same load power. However, the kVA of the deltaconnected autotransformer is less than that of the wyeconnected autotransformer under the same load power.Some experimental results are shown to validate the correctness of the theoretical analysis.