Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 230300013-9.doi: 10.11896/jsjkx.230300013

• Information Security • Previous Articles     Next Articles

Deepfake Images Detection Based on Quantitative Data Features Statistics

XIE Fei, GAO Shuhui   

  1. School of Investigation,People's Public Security University of China,Beijing 100038,China
  • Published:2023-11-09
  • About author:XIE Fei,born in 1997,postgraduate.Her main research interest is forensic science of image.
    GAO Shuhui,born in 1971,Ph.D,professor,Ph.D supervisor.Her mainresearch interest is forensic science of image.
  • Supported by:
    The work was supported by the Double First-class Innovation Research Project of Forensic Science,People’s Public Security University of China(2023SYL06).

PDF (PC)

492

Abstract: Due to the characteristics of “low threshold,high efficiency and high simulation”,deepfake technology is abused to forge identity,the personal information security problems caused by it are bringing serious challenges to public security gover-nance.At present,the mainstream detection of deepfake images is mainly convolution features,while quantitative features are rarely used,which have the advantages of small space and low operation cost.This paper explores the correlation degree of the texture,color features and image authenticity of the images,selects the effective features for the automatic detection of deepfake images,and studies the application value of the quantitative features in the deepfake images identification.40 000 images in the ForgeryNet dataset are used as experimental samples,which are divided into four groups.Texture features and color features in Gray,YCrCb,Lab,HSV and RGB color space of each group of images are extracted,and features with both significant difference and correlation are screened by Mann-Whitney U test and point biserial correlation analysis.Then XGBoost,logistic regression classifier,linear SVM,multilayer perceptron and TabNet are used to verify the seleted features,and finally compared with the mainstream convolutional neural network.Among the five algorithms,MLP and LP are less effective.XGBoost and LSVM are better.TabNet is unstable and greatly affected by classification type,with accuracy ranging from 52% to 89%.The accuracy of the features selected based on mathematical statistics is improved.For example,in the true and false image group,the screening features and texture features in the verification of XGBoost is 1.10% and 1.43% higher than all the features,respectively.The accuracy of texture features verified by LSVM and MLP improves by 0.12% and 0.10%,respectively.The accuracy of the structured feature algorithm based on screening is higher than that of the mainstream convolutional neural network,and the result of texture features is better than that of color features.It is easier to recognize the deepfake image with identity replacement.

Key words: Image texture features, Image color features, Deep fake detection, Data Statistics, Algorithm comparison

CLC Number: 

  • TP309
[1]KIETZMANN J H,MILLS A J,PLANGGER K.Deepfakes:perspectives on the future “reality” of advertising and branding[J].International Journal of Advertising,2020,40:473-485.
[2]FANG Y M.Challenges and Solutions of Deepfake to the Security of Face Recognition Payment System[J].FinTech Time,2020,28(3):13-17.
[3]GUO J L,WANG H R,DOU J S,et al.Development and military application of deep forgery generation and recognition technology[C]//The first Conference of Systems Engineering－New Generation of Intelligent Technology and Systems Engineering.2019:11.
[4]PENG C L,GAO X B,WANG N N,et al.Deep visual identity forgery and detection[J].Scientia Sinica(Informationis),2021,51(9):1451-1474.
[5]LIU Z,QI X,JIA J,et al.Global Texture Enhancement for Fake Face Detection in the Wild[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).2020:8057-8066.
[6]MCCLOSKEY S,ALBRIGHT M.Detecting GAN-generatedImagery using Color Cues[J].arXiv:1812,08247,2018.
[7]ZHOU P,HAN X,MORARIUV I,et al.Two-Stream NeuralNetworks for Tampered Face Detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).2017:1831-1839.
[8]LI Y,LYU S.Exposing DeepFake Videos By Detecting FaceWarping Artifacts[J].arXiv:1811.00656,2019.
[9]KHALID H,WOO S S.OC-FakeDect:Classifying DeepfakesUsing One-class Variational Autoencoder[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).2020:2794-2803.
[10]CHEN C,MCCLOSKEY S,YU J.Focus Manipulation Detection via Photometric Histogram Analysis[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.2018:1674-1682.
[11]CHANG X,WU J,YANG T,et al.DeepFake Face Image Detection based on Improved VGG Convolutional Neural Network[C]//The 39th China Control Conference.Shenyang,China,2020.
[12]MATERN F,RIESS C,STAMMINGER M.Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations[C]//2019 IEEE Winter Applications of Computer Vision Workshops(WACVW).2019:83-92.
[13]SONGSRI-IN K,ZAFEIRIOU S.Complement Face ForensicDetection and Localization with FacialLandmarks[J].arXiv:1910.05455,2019.
[14]YANG X,LI Y,QI H,et al.Exposing GAN-synthesized Faces Using Landmark Locations[C]//Proceedings of the ACM Workshop on Information Hiding and Multimedia Security.2019.
[15]YANG X,LI Y,LYU S.Exposing Deep Fakes Using Inconsistent Head Poses[C]//2019 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP 2019 ).2019:8261-8265.
[16]HU S,LI Y,LYU S.Exposing GAN-Generated Faces Using In-consistent Corneal Specular Highlights[C]//2021 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP 2021).2021:2500-2504.
[17]LEE S Y,TARIQ S,SHIN Y,et al.Detecting handcrafted facial image manipulations and GAN-generated facial images using Shallow-FakeFaceNet[J].Applied Soft Computing,2021,105:107256.
[18]LI H,LI B,TAN S,et al.Identification of deep network generated images using disparities in color components[J].Signal Processing,2020,174:107616.
[19]LI L,BAO J,ZHANG T,et al.Face X-Ray for More GeneralFace Forgery Detection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).2020:5000-5009.
[20]HE P,LI H,WANG H.Detection of Fake Images Via The Ensemble of Deep Representations from Multi Color Spaces[C]//2019 IEEE International Conference on Image Processing (ICIP).2019:2299-2303.
[21]ZHU X T,TANG Y Q,GENG P Z.Detection Algorithm ofTamper and Deepfake Images Based on Feature Fusion[J].Netinfo Security,2021.21(8):70-81.
[22]YANG Y X,ZHOU X,XIONG S H,et al.Research on deepfakes detection combining traditional features and neural network[J].Information Technology and Network Security,2021,40(2):33-38.
[23]ZHANG Y,JIN X,JIANG Q,et al.Deepfake image detectionmethod based on autoencoder[J].Journal of Computer Application,2021,41(10):2985-2990.
[24]DURALL R,KEUPER M,PFREUNDT F,et al.UnmaskingDeepFakes with Simple Features[J].arXiv:1911.00686,2019.
[25]PENG S F,CAI M C,MA R,et al.Deepfake detection algorithm for high-frequency components of shallow features[J/OL].Laser & Optoelectronics Progress.[2022-12-25].http://kns.cnki.net/kcms/detail/31.1690.TN.20220713.1942.593.html.
[26]QIAN Y,YIN G,SHENG L,et al.Thinking in Frequency:Face Forgery Detection by Mining Frequency-aware Clues[C]//ECCV.2020.
[27]WANG L N,NIE J S,WANG R,et al.Analyzing deepfake pro-venance and forensics[J].Journal of Tsinghua University (Science and Technology),2022(5):62.
[28]YU N,DAVIS L,FRITZ M.Attributing Fake Images to GANs:Learning and Analyzing GAN Fingerprints[C]//2019 IEEE/CVF International Conference on Computer Vision(ICCV).2019:7555-7565.
[29]OLIVER G,LUCA G,SEBASTIANO B.Fighting Deepfakes by Detecting GAN DCT Anomalies[J].Journal of Imaging,2021,7(8):128.
[30]HSU C,LEE C,ZHUANG Y.Learning to Detect Fake FaceImages in the Wild[C]//2018 International Symposium on Computer,Consumer and Control(IS3C).2018:388-391.
[31]GUARNERA L,GIUDICE O,BATTIATO S.Fighting Deep-fake by Exposing the Convolutional Traces on Images[J].IEEE Access,2020,8:165085-165098.
[32]WANG S,WANG O,ZHANG R,et al.CNN-Generated Images Are Surprisingly Easy to Spot& for Now[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).2020:8692-8701.
[33]HE Y,GAN B,CHEN S,et al.ForgeryNet:A Versatile Benchmark for Comprehensive Forgery Analysis[J].arXiv:2103.05630,2021.
[34]YANG S,CHEN F.Analyzing Sentiments of Micro-blog Posts Based on Support Vector Machine[J].Data Analysis and Knowledge Discovery,2017,1(2):73-79.
[35]ZHAO J X.Detect of Internet Fake Public Opinion Based on Decision Tree[J].Data Analysis and Knowledge Discovery,2015,259(6):78-84.
[36]CHEN T,GUESTRIN C.XGBoost:A Scalable Tree Boosting System[C]//Proceedings of the 22nd ACM SIGKDD Interna-tional Conference on Knowledge Discovery and Data Mining.2016.
[37]ARIK S Ö,PFISTERT.TabNet:Attentive Interpretable Tabular Learning[J].arXiv:1908.07442,2021.
[38]CAO S H,LIU X H,MAO X Q,et al.A review of human face forgery and forgery-detection technologies[J].Journal of Image and Graphics,2022,27(4):1023-1038.
[1] ZHAO Geng, GAO Shirui, MA Yingjie, DONG Youheng. Design of Dynamic S-box Based on Anti-degradation Chaotic System and Elementary Cellular Automata [J]. Computer Science, 2023, 50(11): 333-339.
[2] GAO Zhen, CHEN Fucai, WANG Yawen, HE Weizhen. VPN Traffic Hijacking Defense Technology Based on Mimic Defense [J]. Computer Science, 2023, 50(11): 340-347.
[3] ZHANG Bolin, LI Bin, YAN Yunfei, WEI Yuanxin, ZHOU Qinglei. ZUC High Performance Data Encryption Scheme Based on FPGA [J]. Computer Science, 2023, 50(11): 374-382.
[4] SUN Yunxiao, LI Jun, WANG Bailing. IPSec VPN Closure Detection Method Based on Side-channel Features [J]. Computer Science, 2023, 50(10): 308-314.
[5] YIN Shiyu, ZHU Youwen, ZHANG Yue. Utility-optimized Local Differential Privacy Joint Distribution Estimation Mechanisms [J]. Computer Science, 2023, 50(10): 315-326.
[6] DING Xuhui, ZHANG Linlin, ZHAO Kai, WANG Xusheng. Android Application Privacy Disclosure Detection Method Based on Static and Dynamic Combination [J]. Computer Science, 2023, 50(10): 327-335.
[7] CHEN Ruixiang, JIAO Jian, WANG Ruohua. Smart Contract Vulnerability Detection System Based on Ontology Reasoning [J]. Computer Science, 2023, 50(10): 336-342.
[8] XU Rui, PENG Changgen, XU Dequan. Authenticated Encryption Scheme of Self-synchronous-like ZUC Algorithm [J]. Computer Science, 2023, 50(10): 377-382.
[9] ZHONG Yue, GU Jieming, CAO Honglin. Survey of Lightweight Block Cipher [J]. Computer Science, 2023, 50(9): 3-15.
[10] HUANG Shuxin, ZHANG Quanxin, WANG Yajie, ZHANG Yaoyuan, LI Yuanzhang. Research Progress of Backdoor Attacks in Deep Neural Networks [J]. Computer Science, 2023, 50(9): 52-61.
[11] LU Xingyuan, CHEN Jingwei, FENG Yong, WU Wenyuan. Privacy-preserving Data Classification Protocol Based on Homomorphic Encryption [J]. Computer Science, 2023, 50(8): 321-332.
[12] WANG Yufang, LE Deguang, Jack TAN, XIAO Le, GONG Shengrong. Opaque Predicate Construction Algorithm Without Size Constraints [J]. Computer Science, 2023, 50(8): 352-358.
[13] LIU Yingjun, LUO Yang, YANG Yujun, LIU Yuanni. Anonymous Authentication Protocol for Medical Internet of Things [J]. Computer Science, 2023, 50(8): 359-364.
[14] YUAN Jiangfeng, LI Haoxiang, YOU Wei, HUANG Jianjun, SHI Wenchang, LIANG Bin. Locating Third-party Library Functions in Obfuscated Applications [J]. Computer Science, 2023, 50(7): 293-301.
[15] WANG Shaohui, ZHAO Zhengyu, WANG Huaqun, XIAO Fu. Analysis and Improvement on Identity-based Remote Data Integrity Verification Scheme [J]. Computer Science, 2023, 50(7): 302-307.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.