A Blind Forgery Detection Scheme Using Image Compatibility Metrics

被引:0
|
作者
Doyoddorj, Munkhbaatar [1 ]
Rhee, Kyung-Hyune [1 ]
机构
[1] Pukyong Natl Univ, Dept Informat Secur, Pusan, South Korea
关键词
EXPOSING DIGITAL FORGERIES;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In order to check the integrity of a digital photograph, a blind forgery detection scheme for forensics is presented in this paper. The proposed scheme is exposing image fakes by using two different terms of compatibility metrics, such as inconsistencies of edge blur width and lighting direction, respectively. The inconsistency metric of an edge blur is constructed based on the deviation from its linear fitting by measuring the blurriness of each pixel along a doubtable edge. Then such metric is used as an evidence for identifying discontinuity of edge in image slicing manipulation. When creating a digital composition of images, it is often difficult to exactly compose the lighting effects due to directional lighting. The lighting inconsistencies can be an useful tool for revealing traces of image composition. Therefore, the direction of the lighting source can be estimated for different objects/people in an image. In fact, the inconsistency of lighting direction has a close relationship with image illumination, which is then used as an evidence of image composition. Experimental results of the proposed scheme on natural images are presented for comparing with other related methods.
引用
收藏
页数:6
相关论文
共 50 条
  • [41] Training Data Improvement for Image Forgery Detection using Comprint
    Mareen, Hannes
    Bussche, Dante Vanden
    Van Wallendael, Glenn
    Verdoliva, Luisa
    Lambert, Peter
    2023 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS, ICCE, 2023,
  • [42] An Image Copy Move Forgery Detection Method Using QDCT
    Li, Ce
    Ma, Qiang
    Xiao, Limei
    Ying, Shihui
    8TH INTERNATIONAL CONFERENCE ON INTERNET MULTIMEDIA COMPUTING AND SERVICE (ICIMCS2016), 2016, : 5 - 8
  • [43] Image forgery detection confronts image composition
    Schetinger, Victor
    Iuliani, Massimo
    Piva, Alessandro
    Oliveira, Manuel M.
    COMPUTERS & GRAPHICS-UK, 2017, 68 : 152 - 163
  • [44] Image splicing forgery detection using noise level estimation
    Kunj Bihari Meena
    Vipin Tyagi
    Multimedia Tools and Applications, 2023, 82 : 13181 - 13198
  • [45] FORGERY DETECTION USING CHAOTIC WATERMARKING IN IMAGE KEY AREAS
    Tao, Rui
    Sun, Yanjing
    Liu, Weidong
    TEHNICKI VJESNIK-TECHNICAL GAZETTE, 2017, 24 (04): : 1263 - 1268
  • [46] Enhancing Digital Image Forgery Detection Using Transfer Learning
    Khalil, Ashgan H.
    Ghalwash, Atef Z.
    Elsayed, Hala Abdel-Galil
    Salama, Gouda I.
    Ghalwash, Haitham A.
    IEEE ACCESS, 2023, 11 : 91583 - 91594
  • [47] Digital Image Forgery Detection using Convolution Neural Networks
    Jyothi, S.
    PalaganiJayavani
    Jitendrakumar, Bubathula
    Venugopla, Kantipudi
    Lavanya, Kampa
    INTERNATIONAL JOURNAL OF EARLY CHILDHOOD SPECIAL EDUCATION, 2022, 14 (04) : 1358 - 1365
  • [48] Analysis of image forgery detection using convolutional neural network
    Gnaneshwar C.
    Singh M.K.
    Yadav S.S.
    Balabantaray B.K.
    International Journal of Applied Systemic Studies, 2022, 9 (03) : 240 - 260
  • [49] Framework For Image Forgery Detection And Classification Using Machine Learning
    Ranjan, Shruti
    Garhwal, Prayati
    Bhan, Anupama
    Arora, Monika
    Mehra, Anu
    PROCEEDINGS OF THE 2018 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS), 2018, : 1872 - 1877
  • [50] Digital image forgery detection using passive techniques: A survey
    Birajdar, Gajanan K.
    Mankar, Vijay H.
    DIGITAL INVESTIGATION, 2013, 10 (03) : 226 - 245