23.3.10.1 Forgery Detection for Images

Chapter Contents (Back)
Forgery Detection. Specifically copy-move: See also Copy-Move Tamper Detection, Forensics.

Yerushalmy, I.[Ido], Hel-Or, H.[Hagit],
Digital Image Forgery Detection Based on Lens and Sensor Aberration,
IJCV(92), No. 1, March 2011, pp. 71-91.
WWW Link. 1103
BibRef

Lin, G.S.[Guo-Shiang], Chang, M.K.[Min-Kuan], Chen, Y.L.[You-Lin],
A Passive-Blind Forgery Detection Scheme Based on Content-Adaptive Quantization Table Estimation,
CirSysVideo(21), No. 4, April 2011, pp. 421-434.
IEEE DOI 1104
BibRef
Earlier:
Image Forgery Detection Based on Quantization Table Estimation,
MVA09(66-).
PDF File. 0905
BibRef

Yao, H.[Heng], Wang, S.Z.[Shuo-Zhong], Zhao, Y.[Yan], Zhang, X.P.[Xin-Peng],
Detecting Image Forgery Using Perspective Constraints,
SPLetters(19), No. 3, March 2012, pp. 123-126.
IEEE DOI 1202
BibRef

Qazi, T., Hayat, K., Khan, S.U., Madani, S.A., Khan, I.A., Kolodziej, J., Li, H., Lin, W., Yow, K.C., Xu, C.Z.,
Survey on blind image forgery detection,
IET-PR(7), No. 7, October 2013, pp. 660-670.
DOI Link 1312
Survey, Forgery Detection. image forensics BibRef

Qureshi, M.A.[Muhammad Ali], Deriche, M.[Mohamed],
A bibliography of pixel-based blind image forgery detection techniques,
SP:IC(39, Part A), No. 1, 2015, pp. 46-74.
Elsevier DOI 1512
Image forensics BibRef

Li, Y.M.[Yuan-Man], Zhou, J.T.[Jian-Tao],
Anti-Forensics of Lossy Predictive Image Compression,
SPLetters(22), No. 12, December 2015, pp. 2219-2223.
IEEE DOI 1512
data compression. Compression can remove protections. BibRef

Pun, C.M.[Chi-Man], Liu, B.[Bo], Yuan, X.C.[Xiao-Chen],
Multi-scale noise estimation for image splicing forgery detection,
JVCIR(38), No. 1, 2016, pp. 195-206.
Elsevier DOI 1605
Splicing forgery BibRef

Emam, M.[Mahmoud], Han, Q.[Qi], Yu, L.Y.[Li-Yang], Zhang, H.L.[Hong-Li],
A Keypoint-Based Region Duplication Forgery Detection Algorithm,
IEICE(E99-D), No. 9, September 2016, pp. 2413-2416.
WWW Link. 1609
BibRef

Aghamaleki, J.A.[Javad Abbasi], Behrad, A.[Alireza],
Inter-frame video forgery detection and localization using intrinsic effects of double compression on quantization errors of video coding,
SP:IC(47), No. 1, 2016, pp. 289-302.
Elsevier DOI 1610
Double compression BibRef

Chen, S., Tan, S., Li, B., Huang, J.,
Automatic Detection of Object-Based Forgery in Advanced Video,
CirSysVideo(26), No. 11, November 2016, pp. 2138-2151.
IEEE DOI 1609
Detectors BibRef

Salloum, R.[Ronald], Ren, Y.[Yuzhuo], Kuo, C.C.J.[C.C. Jay],
Image Splicing Localization using a Multi-task Fully Convolutional Network (MFCN),
JVCIR(51), 2018, pp. 201-209.
Elsevier DOI 1802
Image splicing, Image forensics, Convolutional neural network (CNN), Multi-task network BibRef

Shein, E.[Esther],
The State of Fakery,
CACM(61), No. 3, March 2018, pp. 21-23.
DOI Link 1804
Authenticate digital media BibRef

de Rezende, E.R.S.[Edmar R.S.], Ruppert, G.C.S.[Guilherme C.S.], Theóphilo, A.[Antônio], Tokuda, E.K.[Eric K.], Carvalho, T.[Tiago],
Exposing computer generated images by using deep convolutional neural networks,
SP:IC(66), 2018, pp. 113-126.
Elsevier DOI 1806
Digital forensics, CG detection, Deep learning, Transfer learning, Fake news BibRef

Ding, X., Yang, G., Li, R., Zhang, L., Li, Y., Sun, X.,
Identification of Motion-Compensated Frame Rate Up-Conversion Based on Residual Signals,
CirSysVideo(28), No. 7, July 2018, pp. 1497-1512.
IEEE DOI 1807
Bit rate, Forensics, Forgery, Image edge detection, Markov processes, Videos, Blind video forensics, residual signal and classification BibRef

Cristin, R.[Rajan], Ananth, J.P.[John Patrick], Raj, V.C.[Velankanni Cyril],
Illumination-based texture descriptor and fruitfly support vector neural network for image forgery detection in face images,
IET-IPR(12), No. 8, August 2018, pp. 1439-1449.
DOI Link 1808
BibRef

Verma, V.[Vinay], Agarwal, N.[Nikita], Khanna, N.[Nitin],
DCT-domain deep convolutional neural networks for multiple JPEG compression classification,
SP:IC(67), 2018, pp. 22-33.
Elsevier DOI 1808
Image forensics, Compression forensics, Deep convolutional neural network (CNN), JPEG forensics, Forgery detection BibRef

Ehret, T.[Thibaud],
Automatic Detection of Internal Copy-Move Forgeries in Images,
IPOL(8), 2018, pp. 167-191.
DOI Link 1808
Code, Forgery Detection. BibRef

He, P.S.[Pei-Song], Jiang, X.H.[Xing-Hao], Sun, T.F.[Tan-Feng], Li, H.L.[Hao-Liang],
Computer Graphics Identification Combining Convolutional and Recurrent Neural Networks,
SPLetters(25), No. 9, September 2018, pp. 1369-1373.
IEEE DOI 1809
Distinguish photographs of reality from computer graphics. directed graphs, feature extraction, image classification, image colour analysis, image representation, image texture, recurrent neural network (RNN) BibRef

Nasiri, M.[Morteza], Behrad, A.[Alireza],
Using Expectation-Maximization for exposing image forgeries by revealing inconsistencies in shadow geometry,
JVCIR(58), 2019, pp. 323-333.
Elsevier DOI 1901
Image forensics, Forgery detection, Shadow geometry, EM algorithm, Image tampering BibRef

Zheng, L.[Lilei], Zhang, Y.[Ying], Thing, V.L.L.[Vrizlynn L.L.],
A survey on image tampering and its detection in real-world photos,
JVCIR(58), 2019, pp. 380-399.
Elsevier DOI 1901
Survey, Forgery Detection. Image tampering detection, Image forgery detection, Image forensics, Image copy-move detection, Image splicing detection BibRef

Fadl, S.M.[Sondos M.], Han, Q.[Qi], Li, Q.[Qiong],
Inter-frame forgery detection based on differential energy of residue,
IET-IPR(13), No. 3, February 2019, pp. 522-528.
DOI Link 1903
BibRef

Tu, B.[Bing], He, D.[Danbing], Shang, Y.[Yongheng], Zhou, C.[Chengle], Li, W.[Wujing],
Deep feature representation for anti-fraud system,
JVCIR(59), 2019, pp. 253-256.
Elsevier DOI 1903
Convolutional neural network, Anti-fraud, Distance metric BibRef

George, S.[Sabu], Pai, M.M.M.[M.M. Manohara], Pai, R.M.[Radhika M.], Praharaj, S.K.[Samir Kumar],
Visual cues-based deception detection using two-class neural network,
IJCVR(9), No. 2, 2019, pp. 132-151.
DOI Link 1904
BibRef

Bappy, J.H., Simons, C., Nataraj, L., Manjunath, B.S., Roy-Chowdhury, A.K.,
Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries,
IP(28), No. 7, July 2019, pp. 3286-3300.
IEEE DOI 1906
data compression, decoding, image coding, image resolution, image segmentation, learning (artificial intelligence), decoder BibRef

Hosny, K.M.[Khalid M.], Hamza, H.M.[Hanaa M.], Lashin, N.A.[Nabil A.],
Copy-for-duplication forgery detection in colour images using QPCETMs and sub-image approach,
IET-IPR(13), No. 9, 18 July 2019, pp. 1437-1446.
DOI Link 1907
BibRef

He, P., Li, H., Wang, H.,
Detection of Fake Images Via The Ensemble of Deep Representations from Multi Color Spaces,
ICIP19(2299-2303)
IEEE DOI 1910
generative adversarial network, fake image detection, multi color spaces, random forest BibRef

Asghar, K.[Khurshid], Sun, X.[Xianfang], Rosin, P.L.[Paul L.], Saddique, M.[Mubbashar], Hussain, M.[Muhammad], Habib, Z.[Zulfiqar],
Edge-texture feature-based image forgery detection with cross-dataset evaluation,
MVA(30), No. 7-8, October 2019, pp. 1243-1262.
Springer DOI 1911
BibRef

Prakash, C.S.[Choudhary Shyam], Om, H.[Hari], Maheshkar, S.[Sushila],
Authentication of medical images using passive approach,
IET-IPR(13), No. 13, November 2019, pp. 2420-2427.
DOI Link 1911
Watermarks may interfere with the content of the medical image. polar cosine transform is used for feature extraction and the PatchMatch algorithm to locate the forged. BibRef


McCloskey, S., Albright, M.,
Detecting GAN-Generated Imagery Using Saturation Cues,
ICIP19(4584-4588)
IEEE DOI 1910
BibRef

Jenni, S.[Simon], Favaro, P.[Paolo],
Self-Supervised Feature Learning by Learning to Spot Artifacts,
CVPR18(2733-2742)
IEEE DOI 1812
Real images vs. synthetic. Maintenance engineering, Decoding, Task analysis, Training, Feature extraction, Image segmentation BibRef

Roy, A., Tariang, D.B., Chakraborty, R.S., Naskar, R.,
Discrete Cosine Transform Residual Feature Based Filtering Forgery and Splicing Detection in JPEG Images,
PRIV18(1633-16338)
IEEE DOI 1812
Discrete cosine transforms, Feature extraction, Forgery, Splicing, Transform coding, Histograms, Forensics BibRef

Privman-Horesh, N., Haider, A., Hel-Or, H.,
Forgery Detection in 3D-Sensor Images,
PRIV18(1642-16428)
IEEE DOI 1812
Cameras, Forgery, Sensors, Lighting BibRef

Artaud, C., Sidère, N., Doucet, A., Ogier, J., Yooz, V.P.D.,
Find it! Fraud Detection Contest Report,
ICPR18(13-18)
IEEE DOI 1812
Task analysis, Forgery, Tools, XML, Companies, Optical character recognition software, Training BibRef

Zhang, Z., Zhang, Y., Zhou, Z., Luo, J.,
Boundary-based Image Forgery Detection by Fast Shallow CNN,
ICPR18(2658-2663)
IEEE DOI 1812
Forgery, Image edge detection, Image resolution, Feature extraction, Image coding, Signal processing BibRef

Park, J.[Jinseok], Cho, D.[Donghyeon], Ahn, W.[Wonhyuk], Lee, H.K.[Heung-Kyu],
Double JPEG Detection in Mixed JPEG Quality Factors Using Deep Convolutional Neural Network,
ECCV18(VI: 656-672).
Springer DOI 1810
BibRef

Kuznetsov, A.[Andrey],
Camera Sensor Traces Analysis in Image Forgery Detection Problem,
ICCVG18(453-463).
Springer DOI 1810
BibRef

Maigrot, C., Kijak, E., Claveau, V.,
Context-Aware Forgery Localization in Social-Media Images: A Feature-Based Approach Evaluation,
ICIP18(545-549)
IEEE DOI 1809
Social network services, Splicing, Forgery, Kernel, Estimation, Image forensics, Measurement, Forgery localization, Image tampering BibRef

Long, C., Smith, E., Basharat, A., Hoogs, A.,
A C3D-Based Convolutional Neural Network for Frame Dropping Detection in a Single Video Shot,
MedForen17(1898-1906)
IEEE DOI 1709
Cameras, Color, Feature extraction, Forgery, Histograms, Support vector machines, Training BibRef

Bunk, J., Bappy, J.H., Mohammed, T.M., Nataraj, L., Flenner, A., Manjunath, B.S., Chandrasekaran, S., Roy-Chowdhury, A.K., Peterson, L.,
Detection and Localization of Image Forgeries Using Resampling Features and Deep Learning,
MedForen17(1881-1889)
IEEE DOI 1709
Feature extraction, Heating systems, Image segmentation, Machine learning, Neural networks, Radon, Transform, coding BibRef

Zhou, J.H.[Jiang-Hong], Ni, J.Q.[Jiang-Qun], Rao, Y.[Yuan],
Block-Based Convolutional Neural Network for Image Forgery Detection,
IWDW17(65-76).
Springer DOI 1708
BibRef

Vieira, R.[Rafael], Antunes, M.[Mário], Silva, C.[Catarina], Assis, A.[Ana],
Automatic Documents Counterfeit Classification Using Image Processing and Analysis,
IbPRIA17(400-407).
Springer DOI 1706
BibRef

Cattaneo, G.[Giuseppe], Roscigno, G.[Gianluca], Bruno, A.[Andrea],
Using PNU-Based Techniques to Detect Alien Frames in Videos,
ACIVS16(735-746).
Springer DOI 1611
Apply techniques like in camera id BibRef

Cattaneo, G.[Giuseppe], Ferraro Petrillo, U., Roscigno, G.[Gianluca], de Fusco, C.,
A PNU-based technique to detect forged regions in digital images,
ACIVS15(486-498)
Springer DOI 1611
BibRef

Julliand, T.[Thibault], Nozick, V.[Vincent], Talbot, H.[Hugues],
Automatic Image Splicing Detection Based on Noise Density Analysis in Raw Images,
ACIVS16(126-134).
Springer DOI 1611
BibRef

Buchana, P., Cazan, I., Diaz-Granados, M., Juefei-Xu, F., Savvides, M.,
Simultaneous forgery identification and localization in paintings using advanced correlation filters,
ICIP16(146-150)
IEEE DOI 1610
Art BibRef

Mathai, M., Rajan, D., Emmanuel, S.,
Video forgery detection and localization using normalized cross-correlation of moment features,
Southwest16(149-152)
IEEE DOI 1605
Computers BibRef

Zheng, L.[Lu], Sun, T.[Tanfeng], Shi, Y.Q.[Yun-Qing],
Inter-frame Video Forgery Detection Based on Block-Wise Brightness Variance Descriptor,
IWDW14(18-30).
Springer DOI 1602
BibRef

Wang, W.[Wan], Jiang, X.[Xinghao], Wang, S.L.[Shi-Lin], Wan, M.[Meng], Sun, T.[Tanfeng],
Identifying Video Forgery Process Using Optical Flow,
IWDW13(244-257).
Springer DOI 1407
BibRef

Lin, X.F.[Xu-Feng], Li, C.T.[Chang-Tsun], Hu, Y.J.[Yong-Jian],
Exposing image forgery through the detection of contrast enhancement,
ICIP13(4467-4471)
IEEE DOI 1402
Digital forensics BibRef

Puglisi, G.[Giovanni], Bruna, A.R.[Arcangelo Ranieri], Galvan, F.[Fausto], Battiato, S.[Sebastiano],
First JPEG quantization matrix estimation based on histogram analysis,
ICIP13(4502-4506)
IEEE DOI 1402
Double JPEG Compression; Forgery Identification BibRef

Saleh, S.Q.[Sahar Q.], Hussain, M.[Muhammad], Muhammad, G.[Ghulam], Bebis, G.N.[George N.],
Evaluation of Image Forgery Detection Using Multi-scale Weber Local Descriptors,
ISVC13(II:416-424).
Springer DOI 1311
BibRef

Cozzolino, D.[Davide], Gargiulo, F.[Francesco],
Multiple Classifier Systems for Image Forgery Detection,
CIAP13(II:259-268).
Springer DOI 1309
BibRef

Cozzolino, D.[Davide], Poggi, G.[Giovanni], Sansone, C.[Carlo], Verdoliva, L.[Luisa],
A Comparative Analysis of Forgery Detection Algorithms,
SSSPR12(693-700).
Springer DOI 1211
BibRef

Zach, F.[Fabian], Riess, C.[Christian], Angelopoulou, E.[Elli],
Automated Image Forgery Detection through Classification of Jpeg Ghosts,
DAGM12(185-194).
Springer DOI 1209
BibRef

Al-Hammadi, M.H.[Muneer H.], Muhammad, G.[Ghulam], Hussain, M.[Muhammad], Bebis, G.N.[George N.],
Curvelet Transform and Local Texture Based Image Forgery Detection,
ISVC13(II:503-512).
Springer DOI 1311
BibRef

Polatkan, G.[Gungor], Jafarpour, S.[Sina], Brasoveanu, A.[Andrei], Hughes, S.[Shannon], Daubechies, I.[Ingrid],
Detection of forgery in paintings using supervised learning,
ICIP09(2921-2924).
IEEE DOI 0911
Not through watermarks, but forgery detection. BibRef

Zheng, J.B.[Jiang-Bin], Liu, M.[Miao],
A Digital Forgery Image Detection Algorithm Based on Wavelet Homomorphic Filtering,
DW08(152-160).
Springer DOI 0811
BibRef

Li, Z.[Zhe], Zheng, J.B.[Jiang-Bin],
Blind Detection of Digital Forgery Image Based on the Local Entropy of the Gradient,
DW08(161-169).
Springer DOI 0811
BibRef

Luo, W.Q.[Wei-Qi], Huang, J.W.[Ji-Wu], Qiu, G.P.[Guo-Ping],
A Novel Method for Block Size Forensics Based on Morphological Operations,
DW08(229-239).
Springer DOI 0811
BibRef
Earlier:
Robust Detection of Region-Duplication Forgery in Digital Image,
ICPR06(IV: 746-749).
IEEE DOI 0609
BibRef

Guo, J.K.[Jinhong Katherine],
Forgery Detection by Local Correspondence,
UMD--TR4122, April 2000.
WWW Link. BibRef 0004

Guo, J.K., Doermann, D.S., Rosenfeld, A.,
Off-line Skilled Forgery Detection Using Stroke and Substroke Properties,
ICPR00(Vol II: 355-358).
IEEE DOI 0009
BibRef
Earlier:
Local Correspondence for Detecting Random Forgeries,
ICDAR97(319-323).
IEEE DOI 9708
BibRef

Chapter on OCR, Document Analysis and Character Recognition Systems continues in
Tamper Detection, Forensics .


Last update:Dec 7, 2019 at 17:16:29