http://iet.metastore.ingenta.com
1887

Bibliography of digital image anti-forensics and anti-anti-forensics techniques

Bibliography of digital image anti-forensics and anti-anti-forensics techniques

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

With the massive increase of online content, widespread of social media, the popularity of smartphones, and rise of security breaches, image forensics has attracted a lot of attention in the past two decades alongside the advancements in digital imaging and processing software. The goal is to be able to verify authenticity, ownership, and copyright of an image and detect changes to the original image. However, more sophisticated image manipulation software tools can use subtle anti-forensics techniques (AFTs) to complicate and hinder detection. This leads security professionals and digital investigators to develop more robust forensics tools and counter solutions to defeat adversarial anti-forensics and win the race. This survey study presents a comprehensive systematic overview of various anti-forensics and anti-AFTs that are proposed in the literature for digital image forensics. These techniques are thoroughly analysed based on various important characteristics and grouped into broad categories. This study also presents a bibliographic analysis of the-state-of-the-art publications in various venues. It assists junior researchers in multimedia security and related fields to understand the significance of existing techniques, research trends, and future directions.

References

    1. 1)
      • 1. Stamm, M.C., Wu, M., Liu, K.J.R.: ‘Information forensics: an overview of the first decade’, IEEE. Access., 2013, 1, pp. 167200.
    2. 2)
      • 2. Zhu, B.B., Swanson, M.D., Tewfik, A.H.: ‘When seeing isn't believing [multimedia authentication technologies]’, IEEE Signal Process. Mag., 2004, 21, (2), pp. 4049.
    3. 3)
      • 3. Piva, A.: ‘An overview on image forensics’, ISRN Signal Process., 2013, 2013, pp. 122.
    4. 4)
      • 4. Zheng, L., Zhang, Y., Thing, V.L.: ‘A survey on image tampering and its detection in real-world photos’, J. Vis. Commun. Image Represent., 2019, 58, pp. 380399.
    5. 5)
      • 5. Korus, P.: ‘Digital image integrity – a survey of protection and verification techniques’, Digit. Signal Process., 2017, 71, pp. 126.
    6. 6)
      • 6. Warif, N.B.A., Wahab, A.W.A., Idris, M.Y.I., et al: ‘Copy-move forgery detection: survey, challenges and future directions’, J. Netw. Comput. Appl., 2016, 75, pp. 259278.
    7. 7)
      • 7. Johnston, P., Elyan, E.: ‘A review of digital video tampering: from simple editing to full synthesis’, Digit. Invest., 2019, 29, pp. 6781.
    8. 8)
      • 8. Rocha, A., Scheirer, W., Boult, T., et al: ‘Vision of the unseen: current trends and challenges in digital image and video forensics’, ACM Comput. Surv. (CSUR), 2011, 43, (4), pp. 26:126:42.
    9. 9)
      • 9. Birajdar, G.K., Mankar, V.H.: ‘Digital image forgery detection using passive techniques: a survey’, Digit. Invest., 2013, 10, (3), pp. 226245.
    10. 10)
      • 10. Qureshi, M.A., Deriche, M.: ‘A bibliography of pixel-based blind image forgery detection techniques’, Signal Process., Image Commun., 2015, 39, pp. 4674.
    11. 11)
      • 11. Dixit, R., Naskar, R.: ‘Review, analysis and parameterisation of techniques for copy–move forgery detection in digital images’, IET Image Process., 2017, 11, (9), pp. 746759.
    12. 12)
      • 12. Lin, X., Li, J.H., Wang, S.L., et al: ‘Recent advances in passive digital image security forensics: a brief review’, Engineering, 2018, 4, (1), pp. 2939.
    13. 13)
      • 13. Qureshi, M.A., Deriche, M.: ‘A review on copy move image forgery detection techniques’. 11th Int. Multi-Conf. on Systems, Signals & Devices (SSD). IEEE, Barcelona, Spain, 2014, pp. 15.
    14. 14)
      • 14. Guo, Y., Cao, X., Zhang, W., et al: ‘Fake colorized image detection’, IEEE Trans. Inf. Forensics Sec., 2018, 13, (8), pp. 19321944.
    15. 15)
      • 15. Mahmood, T., Nawaz, T., Mehmood, Z., et al: ‘Forensic analysis of copy-move forgery in digital images using the stationary wavelets’. Proc. 6th IEEE Int. Conf. on Innovative Computing Technology (INTECH), Dublin, Ireland, 2016, pp. 578583.
    16. 16)
      • 16. Farid, H.: ‘Image forgery detection: a survey’, IEEE Signal Process. Mag., 2009, 26, (2), pp. 1625.
    17. 17)
      • 17. Ansari, M.D., Ghrera, S.P., Tyagi, V.: ‘Pixel-based image forgery detection: a review’, IETE J. Educ., 2014, 55, (1), pp. 4046.
    18. 18)
      • 18. Redi, J.A., Taktak, W., Dugelay, J.L.: ‘Digital image forensics: a booklet for beginners’, Multimedia Tools Appl., 2011, 51, (1), pp. 133162.
    19. 19)
      • 19. Dong, J., Wang, W., Tan, T.: ‘CASIA image tampering detection evaluation database’. IEEE China Summit & Int. Conf. on Signal and Information Processing (ChinaSIP). IEEE, 2013, pp. 422426.
    20. 20)
      • 20. Zhu, X., Qian, Y., Zhao, X., et al: ‘A deep learning approach to patch-based image inpainting forensics’, Signal Process., Image Commun., 2018, 67, pp. 9099.
    21. 21)
      • 21. Qureshi, M.A., Deriche, M., Beghdadi, A., et al: ‘A critical survey of state-of-the-art image inpainting quality assessment metrics’, J. Vis. Commun. Image Represent., 2017, 49, pp. 177191.
    22. 22)
      • 22. Pranita, D., Monika, R.: ‘Survey on anti-forensics operations in image forensics’, Int. J. Comput. Sci. Inf. Technol., 2014, 5, pp. 15701573.
    23. 23)
      • 23. Singh, N., Joshi, S.: ‘Digital image forensics and counter antiforensic’. Proc. of Int. Conf. on Recent Cognizance in Wireless Communication and Image Processing, Springer, 2016, pp. 805811.
    24. 24)
      • 24. Gül, M., Kugu, E.: ‘A survey on anti-forensics techniques’. IEEE Int. Artificial Intelligence and Data Processing Symp. (IDAP), Malatya, Turkey, 2017, pp. 16.
    25. 25)
      • 25. Shelke, P.M., Prasad, R.S.: ‘Tradeoffs between forensics and anti-forensics of digital images’. Computer Vision: Concepts, Methodologies, Tools, and Applications. IGI Global, IGI Global, Pennsylvania, USA, 2018, pp. 21242138.
    26. 26)
      • 26. Böhme, R., Kirchner, M.: ‘Counter-forensics: attacking image forensics’. Digital Image Forensics, New York, 2013, pp. 327366.
    27. 27)
      • 27. Cao, G., Zhao, Y., Ni, R., et al: ‘Contrast enhancement-based forensics in digital images’, IEEE Trans. Inf. Forensics Sec., 2014, 9, (3), pp. 515525.
    28. 28)
      • 28. Stamm, M.C., Liu, K.J.R.: ‘Blind forensics of contrast enhancement in digital images’. IEEE Int. Conf. on Image Processing, San Diego, CA, USA, 2008, pp. 31123115.
    29. 29)
      • 29. Stamm, M.C., Liu, K.J.R.: ‘Forensic detection of image manipulation using statistical intrinsic fingerprints’, IEEE Trans. Inf. Forensics Secur., 2010, 5, (3), pp. 492506.
    30. 30)
      • 30. Lin, X., Li, C.T., Hu, Y.: ‘Exposing image forgery through the detection of contrast enhancement’. Proc. 20th IEEE Int. Conf. on Image Processing (ICIP), Melbourne, VIC, Australia, 2013, pp. 44674471.
    31. 31)
      • 31. Cao, G., Zhao, Y., Ni, R., et al: ‘Anti-forensics of contrast enhancement in digital images’. Proc. of the 12th ACM Workshop on Multimedia and Security (MM&Sec), Rome, Italy, 2010, pp. 2534.
    32. 32)
      • 32. Cao, G., Zhao, Y., Ni, R., et al: ‘Attacking contrast enhancement forensics in digital images’, Sci. China Inform. Sci., 2014, 57, (5), pp. 113.
    33. 33)
      • 33. Kwok, C.W., Au, O.C., Chui, S.H.: ‘Alternative anti-forensics method for contrast enhancement’. 10th Int. Workshop on Digital Forensics and Watermarking, (vol. 7128 LNCS, Atlantic City, NY, USA, 2011), pp. 398410.
    34. 34)
      • 34. Barni, M., Fontani, M., Tondi, B.: ‘A universal technique to hide traces of histogram-based image manipulations’. Proc. 14th ACM Workshop on Multimedia and Security (MM&Sec), Coventry, UK, 2012, pp. 97104.
    35. 35)
      • 35. Barni, M., Fontani, M., Tondi, B.: ‘A universal attack against histogram-based image forensics’, Int. J Dig Crime Forens, 2013, 5, (3), pp. 3552.
    36. 36)
      • 36. Comesana.Alfaro, P., Pérez.González, F.: ‘Optimal counterforensics for histogram-based forensics’. Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, 2013, pp. 30483052.
    37. 37)
      • 37. De Rosa, A., Fontani, M., Massai, M., et al: ‘Second-order statistics analysis to cope with contrast enhancement counter-forensics’, IEEE Signal Process. Lett., 2015, 22, (8), pp. 11321136.
    38. 38)
      • 38. Ravi, H., Subramanyam, A., Emmanuel, S.: ‘ACE – an effective anti-forensic contrast enhancement technique’, IEEE Signal Process. Lett., 2016, 23, (2), pp. 212216.
    39. 39)
      • 39. Ding, F., Zhu, G., Shi, Y.Q.: ‘A novel method for detecting image sharpening based on local binary pattern’. Int. Workshop on Digital Watermarking, Auckland, New Zealand, 2013, pp. 180191.
    40. 40)
      • 40. Cao, G., Zhao, Y., Ni, R., et al: ‘Unsharp masking sharpening detection via overshoot artifacts analysis’, IEEE Signal Process. Lett., 2011, 18, (10), pp. 603606.
    41. 41)
      • 41. Laijie, L., Gaobo, Y., Ming, X.: ‘Anti-forensics for unsharp masking sharpening in digital images’, Int. J. Digit. Crime Forensics (IJDCF), 2013, 5, (3), pp. 5365.
    42. 42)
      • 42. Kang, X., Stamm, M.C., Peng, A., et al: ‘Robust median filtering forensics using an autoregressive model’, IEEE Trans. Inf. Forensics Sec., 2013, 8, (9), pp. 14561468.
    43. 43)
      • 43. Nguyen, D.T., Gebru, I., Conotter, V., et al: ‘Counter-forensics of median filtering’. IEEE 15th Int. Workshop on Multimedia Signal Processing (MMSP), Pula, Italy, 2013, pp. 260265.
    44. 44)
      • 44. Wu, Z.H., Stamm, M.C., Liu, K.R.: ‘Anti-forensics of median filtering’. Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, BC, 2013, pp. 30433047.
    45. 45)
      • 45. Fan, W., Wang, K., Cayre, F., et al: ‘Median filtered image quality enhancement and anti-forensics via variational deconvolution’, IEEE Trans. Inf. Forensics Sec., 2015, 10, (5), pp. 10761091.
    46. 46)
      • 46. Fontani, M., Barni, M.: ‘Hiding traces of median filtering in digital images’. Proc. of the 20th European Signal Processing Conf. (EUSIPCO), Bucharest, Romania, 2012, pp. 12391243.
    47. 47)
      • 47. Chen, C., Ni, J., Huang, J.: ‘Blind detection of median filtering in digital images: a difference domain based approach’, IEEE Trans. Image Process., 2013, 22, (12), pp. 46994710.
    48. 48)
      • 48. Yuan, H.D.: ‘Blind forensics of median filtering in digital images’, IEEE Trans. Inf. Forensics Sec., 2011, 6, (4), pp. 13351345.
    49. 49)
      • 49. Zhang, Y., Li, S., Wang, S., et al: ‘Revealing the traces of median filtering using high-order local ternary patterns’, IEEE Signal Process. Lett., 2014, 21, (3), pp. 275279.
    50. 50)
      • 50. Sharma, S., Subramanyam, A.V., Jain, M., et al: ‘Anti-forensic technique for median filtering using L1-L2 TV model’. 8th IEEE Int. Workshop on Information Forensics and Security (WIFS), Abu Dhabi, United Arab Emirates, 2016, pp. 16.
    51. 51)
      • 51. Kim, D., Jang, H.U., Mun, S.M., et al: ‘Median filtered image restoration and anti-forensics using adversarial networks’, IEEE Signal Process. Lett., 2018, 25, (2), pp. 278282.
    52. 52)
      • 52. Johnson, M.K., Farid, H.: ‘Exposing digital forgeries through chromatic aberration’. Proceeding of the 8th Workshop on Multimedia and Security, Geneva, Switzerland, 2006, pp. 4855.
    53. 53)
      • 53. Mayer, O., Stamm, M.: ‘Improved forgery detection with lateral chromatic aberration’. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, 2016, pp. 20242028.
    54. 54)
      • 54. Yerushalmy, I., Hel.Or, H.: ‘Digital image forgery detection based on lens and sensor aberration’, Int. J. Comput. Vis., 2011, 92, (1), pp. 7191.
    55. 55)
      • 55. Mayer, O., Stamm, M.C.: ‘Accurate and efficient image forgery detection using lateral chromatic aberration’, IEEE Trans. Inf. Forensics Sec., 2018, 13, (7), pp. 17621777.
    56. 56)
      • 56. Mayer, O., Stamm, M.C.: ‘Anti-forensics of chromatic aberration’. Media Watermarking, Security, and Forensics, SPIE, San Francisco, California, USA, 2015, vol. 9409, pp. 94090M94090M.
    57. 57)
      • 57. Sengupta, P., Sameer, V.U., Naskar, R., et al: ‘Source anonymization of digital images: a counter–forensic attack on PRNU based source identification techniques’. Annual ADFSL Conf. on Digital Forensics, Security and Law, Florida, USA, 2017, pp. 95106.
    58. 58)
      • 58. Villalba, L.J.G., Orozco, A.L.S., Corripio, J.R., et al: ‘A PRNU-based counter-forensic method to manipulate smartphone image source identification techniques’, Future Gener. Comput. Syst., 2017, 76, pp. 418427.
    59. 59)
      • 59. Tuama, A., Comby, F., Chaumont, M.: ‘Camera model identification with the use of deep convolutional neural networks’. Proc. of IEEE Int. Workshop on Information Forensics and Security (WIFS), Abu Dhabi, United Arab Emirates, 2016, pp. 16.
    60. 60)
      • 60. Chen, C., Zhao, X., Stamm, M.C.: ‘MISLGAN: an anti-forensic camera model falsification framework using a generative adversarial network’. Proc. of the 25th IEEE Int. Conf. on Image Processing (ICIP), Athens, Greece, 2018, pp. 535539.
    61. 61)
      • 61. Dirik, A.E., Memon, N.: ‘Image tamper detection based on demosaicing artifacts’. 16th IEEE Int. Conf. on Image Processing (ICIP), Cairo, Egypt, 2009, pp. 14971500.
    62. 62)
      • 62. Gallagher, A.C., Chen, T.: ‘Image authentication by detecting traces of demosaicing’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops, Anchorage, AK, USA, 2008.
    63. 63)
      • 63. Böhme, R., Kirchner, M.: ‘Synthesis of color filter array pattern in digital images’. Proc. SPIE, Media Forensics and Security XI, San Jose, California, USA, 2009, vol. 7254, pp. 72540K72540K-14.
    64. 64)
      • 64. Guangling, S., Zhoubiao, S., Yuejun, C.: ‘Color filter array synthesis in digital image via dictionary re-demosaicing’. Int. Conf. on Multimedia Information Networking and Security (MINES), Nanjing, Jiangsu, China, 2010, pp. 898901.
    65. 65)
      • 65. Chuang, W.H., Wu, M.: ‘Robustness of color interpolation identification against anti-forensic operations’. Information Hiding (vol. 7692 LNCS, Berkeley, CA, USA, 2012), pp. 1630.
    66. 66)
      • 66. Popescu, A.C., Farid, H.: ‘Exposing digital forgeries by detecting traces of resampling’, IEEE Trans. Signal Process., 2005, 53, (2), pp. 758767.
    67. 67)
      • 67. Gallagher, A.C.: ‘Detection of linear and cubic interpolation in JPEG compressed images’. Second Canadian Conf. on Computer and Robot Vision, Victoria, BC, Canada, 2005, pp. 6572.
    68. 68)
      • 68. Mahdian, B., Saic, S.: ‘Blind authentication using periodic properties of interpolation’, IEEE Trans. Inf. Forensics Sec., 2008, 3, (3), pp. 529538.
    69. 69)
      • 69. Kirchner, M.: ‘Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue’. 10th ACM Workshop on Multimedia and Security, Oxford, UK, 2008, pp. 1120.
    70. 70)
      • 70. Gloe, T., Kirchner, M., Winkler, A., et al: ‘Can we trust digital image forensics?’. 15th Int. Conf. on Multimedia, Newyork, USA, 2007, pp. 7886.
    71. 71)
      • 71. Kirchner, M., Böhme, R.: ‘Hiding traces of resampling in digital images’, IEEE Trans. Inf. Forensics Sec., 2008, 3, (4), pp. 582592.
    72. 72)
      • 72. Thai, T.H., Cogranne, R., Retraint, F., et al: ‘JPEG quantization step estimation and its applications to digital image forensics’, IEEE Trans. Inf. Forensics Sec., 2016, 12, (1), pp. 123133.
    73. 73)
      • 73. Wang, Q., Zhang, R.: ‘Double JPEG compression forensics based on a convolutional neural network’, EURASIP J. Inf. Secur., 2016, 23, pp. 112.
    74. 74)
      • 74. Luo, Y.W., Huang, J.: ‘Detection of quantization artifacts and its applications to transform encoder identification’, IEEE Trans. Inf. Forensics Sec., 2010, 5, (4), pp. 810815.
    75. 75)
      • 75. Ebrahimi, E.G., Ibrahim, S., Alizadeh, M.: ‘Paint-doctored JPEG image forensics based on blocking artifacts’. Int. Conf. and Workshop on Computing and Communication (IEMCON), Vancouver, BC, Canada, 2015, pp. 15.
    76. 76)
      • 76. Singh, N., Bansal, R.: ‘Analysis of Benford's law in digital image forensics’. Int. Conf. on Signal Processing and Communication (ICSC), Noida, India, 2015, pp. 413418.
    77. 77)
      • 77. Stamm, M.C., Tjoa, S.K., Lin, W.S., et alAnti-forensics of JPEG compression’. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Dallas, TX, USA, 2010, pp. 16941697.
    78. 78)
      • 78. Schaefer, G., Stich, M.: ‘UCID: an uncompressed color image database’. Storage and Retrieval Methods and Applications for Multimedia 2004. vol. 5307. Int. Society for Optics and Photonics, San Jose, California, USA, 2003, pp. 472481.
    79. 79)
      • 79. Stamm, M.C., Liu, K.J.R.: ‘Wavelet-based image compression anti-forensics’. 17th Int. Conf. on Image Processing (ICIP), Hong Kong, China, 2010, pp. 17371740.
    80. 80)
      • 80. Stamm, M.C., Liu, K.J.R.: ‘Anti-forensics of digital image compression’, IEEE Trans. Inf. Forensics Sec., 2011, 6, (3), pp. 10501065.
    81. 81)
      • 81. Sutthiwan, P., Shi, Y.Q.: ‘Anti-forensics of double JPEG compression detection’. Int. Workshop on Digital Watermarking, Atlantic City, NJ, 2011, pp. 411424.
    82. 82)
      • 82. Jiang, Y., Zeng, H., Kang, X., et al: ‘The game of countering JPEG anti-forensics based on the noise level estimation’. Signal and Information Processing Association Annual Summit and Conf. (APSIPA), Kaohsiung, Taiwan, 2013, pp. 19.
    83. 83)
      • 83. Fan, W., Wang, K., Cayre, F., et al: ‘A variational approach to JPEG anti-forensics’. Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, BC, Canada, 2013, pp. 30583062.
    84. 84)
      • 84. Fan, Z., de Queiroz, R.L.: ‘Identification of bitmap compression history: JPEG detection and quantizer estimation’, IEEE Trans. Image Process., 2003, 12, (2), pp. 230235.
    85. 85)
      • 85. Barni, M., Fontani, M., Tondi, B.: ‘Universal counterforensics of multiple compressed jpeg images’. 13th Int. Workshop on Digital Forensics and Watermarking, 2015, vol. 9023, pp. 3146.
    86. 86)
      • 86. Qian, Z., Zhang, X.: ‘Improved anti-forensics of JPEG compression’, J. Syst. Softw., 2014, 91, (1), pp. 100108.
    87. 87)
      • 87. Cao, Y., Gao, T., Sheng, G., et al: ‘A new anti-forensic scheme hiding the single JPEG compression trace for digital image’, J. Forensic Sci., 2015, 60, (1), pp. 197205.
    88. 88)
      • 88. Li, Y., Zhou, J.: ‘Anti-forensics of lossy predictive image compression’, IEEE Signal Process. Lett., 2015, 22, (12), pp. 22192223.
    89. 89)
      • 89. Afshin, N., Razzazi, F., Moin, M.S.: ‘A dictionary based approach to JPEG anti-forensics’. IEEE 8th Int. Conf. on Intelligent Systems, (IS), Sofia, Bulgaria, 2016, pp. 778783.
    90. 90)
      • 90. Shelke, P.M., Prasad, R.S.: ‘An improved anti-forensics jpeg compression using least cuckoo search algorithm’, Imaging Sci. J., 2018, 66, (3), pp. 169183.
    91. 91)
      • 91. Das, T.K.: ‘Anti-forensics of JPEG compression detection schemes using approximation of dct coefficients’, Multimedia Tools Appl., 2018, 77, (24), pp. 3183531854.
    92. 92)
      • 92. Bas, P., Filler, T., Pevný, T.: ‘Break our steganographic system: the ins and outs of organizing BOSS’. Int. workshop on information hiding, Prague, Czech Republic, 2011, pp. 5970.
    93. 93)
      • 93. Luo, Y., Zi, H., Zhang, Q., et al: ‘Anti-forensics of jpeg compression using generative adversarial networks’. Proc. of the 26th IEEE European Signal Processing Conf. (EUSIPCO), Rome, Italy, 2018, pp. 952956.
    94. 94)
      • 94. BOWS.: ‘The 2nd BOWS contest (break our watermarking system) was organised within the activity of the water marking virtual laboratory (Wavila) of the European network of excellence ECRYPT between the 17th of July 2007 and 17th of April, 2009’, 2009, http://bows2.ec-lille.fr/.
    95. 95)
      • 95. Li, H., Luo, W., Huang, J.: ‘Anti-forensics of double JPEG compression with the same quantization matrix’, Multimedia Tools Appl., 2015, 74, (17), pp. 67296744.
    96. 96)
      • 96. Singh, G., Singh, K.: ‘Improved JPEG anti-forensics with better image visual quality and forensic undetectability’, Forensic Sci. Int., 2017, 277, pp. 133147.
    97. 97)
      • 97. Stamm, M.C., Tjoa, S.K., Lin, W.S., et al: ‘Undetectable image tampering through JPEG compression anti-forensics’. 17th Int. Conf. on Image Processing (ICIP), Hong Kong, 2010, pp. 21092112.
    98. 98)
      • 98. Manimurugan, S., Athira, B.: ‘A tailored anti-forensic technique for digital image applications’, Int. J. Comput. Appl., 2012, 53, pp. 1420.
    99. 99)
      • 99. Valenzise, G., Tagliasacchi, M., Tubaro, S.: ‘The cost of JPEG compression anti-forensics’. Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Prague, CZECH REPUBLIC, 2011, pp. 18841887.
    100. 100)
      • 100. Fan, W., Wang, K., Cayre, F., et al: ‘JPEG anti-forensics using non-parametric DCT quantization noise estimation and natural image statistics’. Proc. of ACM Information Hiding and Multimedia Security Workshop, Montpellier, France, 2013, pp. 117122.
    101. 101)
      • 101. Fan, W., Wang, K., Cayre, F., et al: ‘JPEG anti-forensics with improved tradeoff between forensic undetectability and image quality’, IEEE Trans. Inf. Forensics Sec., 2014, 9, (8), pp. 12111226.
    102. 102)
      • 102. Zhao, C., Zhang, J., Ma, S., et al: ‘Reducing image compression artifacts by structural sparse representation and quantization constraint prior’, IEEE Trans. Circuits Syst. Video Technol., 2017, 27, (10), pp. 20572071.
    103. 103)
      • 103. Tang, A.W.K.K., Craft, N.: ‘Using a knowledge-based approach to remove blocking artifacts in skin images for forensic analysis’, IEEE Trans. Inf. Forensics Sec., 2011, 6, (3), pp. 10381049.
    104. 104)
      • 104. Gambhir, D., Rajpal, N.: ‘Fuzzy edge detector based blocking artifacts removal of DCT compressed images’. Int. Conf. on Circuits, Controls and Communications (CCUBE), Bengaluru, India, 2013, pp. 16.
    105. 105)
      • 105. Lai, S., Böhme, R.: ‘Countering counter-forensics: the case of JPEG compression’. Information Hiding, Prague, Czech Republic, 2011, vol. 6958, pp. 285298.
    106. 106)
      • 106. Huang, F., Huang, J., Shi, Y.Q.: ‘Detecting double JPEG compression with the same quantization matrix’, IEEE Trans. Inf. Forensics Sec., 2010, 5, (4), pp. 848856.
    107. 107)
      • 107. Feng, C., Xu, Z., Zheng, X.: ‘An anti-forensic algorithm of JPEG double compression based forgery detection’. Int. Symp. on Information Science and Engineering (ISISE), Shanghai, China, 2012, pp. 159164.
    108. 108)
      • 108. Milani, S., Tagliasacchi, M., Tubaro, S.: ‘Antiforensics attacks to Benford's law for the detection of double compressed images’. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, BC, Canada, 2013, pp. 30533057.
    109. 109)
      • 109. Qian, Z., Qiao, T.: ‘Simplified anti-forensics of JPEG compression’, J. Comput. (JCP), 2013, 8, (10), pp. 24832488.
    110. 110)
      • 110. Pedro, C., Fernando, P.G.: ‘The optimal attack to histogram-based forensic detectors is simple(x)’. IEEE Int. Workshop on Information Forensics and Security (WIFS), Atlanta, GA, USA, 2014, pp. 137142.
    111. 111)
      • 111. Pasquini, C., Comesana.Alfaro, P., Pérez.González, F., et al: ‘Transportation-theoretic image counterforensics to first significant digit histogram forensics’. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, 2014, pp. 26992703.
    112. 112)
      • 112. Barni, M., Stamm, M.C., Tondi, B.: ‘Adversarial multimedia forensics: overview and challenges ahead’. Proc. of the 26th IEEE European Signal Processing Conf. (EUSIPCO), Rome, Italy, 2018, pp. 962966.
    113. 113)
      • 113. Gragnaniello, D., Marra, F., Poggi, G., et al: ‘Analysis of adversarial attacks against CNN-based image forgery detectors’. Proc. of 26th European Signal Processing Conf. (EUSIPCO), Rome, Italy, 2018, pp. 967971.
    114. 114)
      • 114. Singh, N., Joshi, S.: ‘Digital image forensics and counter anti-forensics’. Proc. of the Int. Conf. on Recent Cognizance in Wireless Communication & Image Processing, 2016, pp. 805811.
    115. 115)
      • 115. Zeng, H., Qin, T., Kang, X., et al: ‘Countering anti-forensics of median filtering’. Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Springer, 2014, pp. 27042708.
    116. 116)
      • 116. Gloe, T., Böhme, R.: ‘The ‘Dresden image database’ for benchmarking digital image forensics’. Proc. of the 2010 ACM Symp. on Applied Computing. ACM, Sierre, Switzerland, 2010, pp. 15841590.
    117. 117)
      • 117. Kang, X., Qin, T., Zeng, H.: ‘Countering median filtering anti-forensics and performance evaluation of forensics against intentional attacks’. Signal and Information Processing (ChinaSIP), 2015 IEEE China Summit and Int. Conf. on, Chengdu, China, 2015, pp. 483487.
    118. 118)
      • 118. Mayer, O., Stamm, M.C.: ‘Countering anti-forensics of lateral chromatic aberration’. Proc. of the 5th ACM Workshop on Information Hiding and Multimedia Security, Philadelphia, Pennsylvania, USA, 2017, pp. 1520.
    119. 119)
      • 119. Chen, C., Zhao, X., Stamm, M.C.: ‘Detecting anti-forensic attacks on demosaicing-based camera model identification’. IEEE Int. Conf. on Image Processing (ICIP), Beijing, China, 2017, pp. 15121516.
    120. 120)
      • 120. Cao, G., Zhao, Y., Ni, R.: ‘Forensic identification of resampling operators: a semi non-intrusive approach’, Forensic Sci. Int., 2012, 216, (1–3), pp. 2936.
    121. 121)
      • 121. Peng, A., Zeng, H., Lin, X., et al: ‘Countering anti-forensics of image resampling’. IEEE Int. Conf. on Image Processing (ICIP). IEEE, Quebec City, QC, Canada, 2015, pp. 35953599.
    122. 122)
      • 122. Peng, A., Wu, Y., Kang, X.: ‘Revealing traces of image resampling and resampling antiforensics’, Adv. Multimed., 2017, 2017, pp. 113.
    123. 123)
      • 123. Li, H., Luo, W., Huang, J.: ‘Countering anti-JPEG compression forensics’. 19th Int. Conf. on Image Processing (ICIP), Orlando, FL, USA, 2012, pp. 241244.
    124. 124)
      • 124. Valenzise, G., Tagliasacchi, M., Tubaro, S.: ‘Revealing the traces of JPEG compression anti-forensics’, IEEE Trans. Inf. Forensics Sec., 2013, 8, (2), pp. 335349.
    125. 125)
      • 125. ‘NRCS Photo Gallary [online]’, https://photogallery.sc.egov.usda.gov/res/sites/photogallery/.
    126. 126)
      • 126. Wang, M., Chen, Z., Fan, W., et al: ‘Countering anti-forensics to wavelet-based compression’. IEEE Int. Conf. on Image Processing (ICIP), Paris, France, 2014, pp. 53825386.
    127. 127)
      • 127. Fahmy, G., Würtz, R.: ‘Phase based forgery detection of jpeg anti forensics’. IEEE Int. Symp. on Signal Processing and Information Technology (ISSPIT), Limassol, Cyprus, 2016, pp. 16.
    128. 128)
      • 128. Barni, M., Chen, Z., Tondi, B.: ‘Adversary-aware, data-driven detection of double JPEG compression: how to make counter-forensics harder’. IEEE Int. Workshop on Information Forensics and Security (WIFS), Abu Dhabi, United Arab Emirates, 2016, pp. 16.
    129. 129)
      • 129. Dang Nguyen, D.T., Pasquini, C., Conotter, V., et alRaise: a raw images dataset for digital image forensics’. Proc. of the 6th ACM Multimedia Systems Conf. ACM, Portland, Oregon, 2015, pp. 219224.
    130. 130)
      • 130. Zeng, H., Yu, J., Kang, X., et al: ‘Countering JPEG anti-forensics based on noise level estimation’, Sci. China Inform. Sci., 2018, 61, (3), p. 032103.
    131. 131)
      • 131. Bhardwaj, D., Pankajakshan, V.: ‘A JPEG blocking artifact detector for image forensics’, Signal Process., Image Commun., 2018, 68, pp. 155161.
    132. 132)
      • 132. Bhardwaj, D., Kumawat, C., Pankajakshan, V.: ‘A method for detecting JPEG anti-forensics’. 6th National Conf. on Computer Vision, Pattern Recognition, Image Processing, and Graphics, Mandi, India, 2017, pp. 190197.
    133. 133)
      • 133. Li, H., Luo, W., Qiu, X., et al: ‘Identification of various image operations using residual-based features’, IEEE Trans. Circuits Syst. Video Technol., 2018, 28, (1), pp. 3145.
    134. 134)
      • 134. Li, B., Zhang, H., Luo, H., et al: ‘Detecting double JPEG compression and its related anti-forensic operations with CNN’, Multimedia Tools Appl., 2019, 78, (7), pp. 85778601.
    135. 135)
      • 135. Singh, G., Singh, K.: ‘Counter JPEG anti-forensic approach based on the second-order statistical analysis’, IEEE Trans. Inf. Forensics Sec., 2019, 14, (5), pp. 11941209.
    136. 136)
      • 136. Fahmy, G., Alqallaf, A., Wurtz, R.: ‘Phase based detection of JPEG counter forensics’. IEEE Int. Conf. on Electronics, Circuits, and Systems (ICECS), Cairo, Egypt, 2015, pp. 3740.
    137. 137)
      • 137. Valenzise, G., Nobile, V., Tagliasacchi, M., et al: ‘Countering JPEG anti-forensics’. 18th IEEE Int. Conf. on Image Processing (ICIP), Brussels, Belgium, 2011, pp. 19491952.
    138. 138)
      • 138. Rudin, L.I., Osher, S., Fatemi, E.: ‘Nonlinear total variation based noise removal algorithms’, Phys. D, Nonlinear Phenom., 1992, 60, (1), pp. 259268.
    139. 139)
      • 139. Fontani, M., Bonchi, A., Piva, A., et al: ‘Countering anti-forensics by means of data fusion’. Media Watermarking, Security, and Forensics Int. Society for Optics and Photonics, San Francisco, CA, 2014, vol. 9028, pp. 90280Z90280Z–15.
    140. 140)
      • 140. Stamm, M.C., Liu, K.R.: ‘Forensic estimation and reconstruction of a contrast enhancement mapping’. IEEE Int. Conf. on Acoustics Speech and Signal Processing (ICASSP), Dallas, TX, USA, 2010, pp. 16981701.
    141. 141)
      • 141. Sun, J.Y., Kim, S.W., Lee, S.W., et al: ‘A novel contrast enhancement forensics based on convolutional neural networks’, Signal Process., Image Commun., 2018, 63, pp. 149160.
    142. 142)
      • 142. Yang, P., Ni, R., Zhao, Y., et al: ‘Robust contrast enhancement forensics using convolutional neural networks’, arXiv preprint arXiv:180304749, 2018.
    143. 143)
      • 143. Shan, W., Yi, Y., Huang, R., et al: ‘Robust contrast enhancement forensics based on convolutional neural networks’, Signal Process., Image Commun., 2019, 71, pp. 138146.
    144. 144)
      • 144. Akhtar, Z., Khan, E.: ‘Revealing the traces of histogram equalisation in digital images’, IET Image Process., 2017, 12, (5), pp. 760768.
    145. 145)
      • 145. Zeng, H., Kang, X., Peng, A.: ‘A multi-purpose countermeasure against image anti-forensics using autoregressive model’, Neurocomputing, 2016, 189, pp. 117122.
    146. 146)
      • 146. Pevny, T., Bas, P., Fridrich, J.: ‘Steganalysis by subtractive pixel adjacency matrix’, IEEE Trans. Inf. Forensics Sec., 2010, 5, (2), pp. 215224.
    147. 147)
      • 147. Yu, J., Zhan, Y., Yang, J., et al: ‘A multi-purpose image counter-anti-forensic method using convolutional neural networks’. Int. Workshop on Digital Watermarking. (vol. 10082 LNCS, Beijing, China, 2016), pp. 315.
    148. 148)
      • 148. Chen, Y., Kang, X., Wang, Z.J., et al: ‘Densely connected convolutional neural network for multi-purpose image forensics under anti-forensic attacks’. Proc. of the 6th ACM Workshop on Information Hiding and Multimedia Security, Innsbruck, Austria, 2018, pp. 9196.
    149. 149)
      • 149. Li, H., He, P., Wang, S., et al: ‘Learning generalized deep feature representation for face anti-spoofing’, IEEE Trans. Inf. Forensics Sec., 2018, 13, (10), pp. 26392652.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.6587
Loading

Related content

content/journals/10.1049/iet-ipr.2018.6587
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address