CLIFD: A novel image forgery detection technique using digital signatures

  • Sahib Khan Department of Electronics and Telecommunications, Politecnico di Torino, Italy
Keywords: Forgery detection, digital signature, least significant bits (LSB) substitution, message digest 5 (MD5).


This paper is presenting a new image forgery detection technique. The proposed technique makes use of digital signatures, it generates a digital signature for each column and embeds the signature in the least significant bits of selected pixels of each corresponding column. The message digest five algorithm is used for digital signature generation and four least significant bits substitution mechanism is used to embed the signature in the designated pixels. The embedding of the digital signature in the selected pixel remains completely innocent and undetectable for the human visual system. The proposed forgery detection technique has demonstrated significant results against different types of forgeries introduced to digital images and successfully detected and pointed out the forged columns.


Farid, H. 2009. Image forgery detection. IEEE Signal Processing Magazine. 26(2): 16-25.

Redi, J.A., Taktak, W. & Dugelay, J.L. 2011. Digital image forensics: A booklet for beginners. Multimedia Tool Appl. 51(1): 133-162.

Wang, J., Liu, G., Zhang, Z., Dai, Y., & Wang, Z. 2009. Fast and robust forensics for image region-duplication forgery. Acta Automatica Sinica. 35(12): 1488-1495.

Farid, H. 2009. A survey of image forgery detection. IEEE Signal Process. Mag. 6(2): 16-25.

Khan, S., Ismail, M., Khan, T., & Ahmad, N. 2016. Enhanced stego block chaining (ESBC) for low bandwidth channels. Security and Communication Networks. 9(18): 6239-6247).

Khan, S., Ahmad, N., Ismail, M., Minallah, N., & Khan, T. 2015. A secure true edge based 4 least significant bits steganography. In International Conference on Emerging Technologies (ICET), Peshawar, Pakistan.: 1-4. IEEE.

Wahid, M., Ahmad, N., Zafar, M.H., & Khan, S. 2018. On combining MD5 for image authentication using LSB substitution in selected pixels. In International Conference on Engineering and Emerging Technologies (ICEET). Lahore, Pakistan. :1-6. IEEE.

Birajdar, G.K., & Mankar, V.H. 2013. Digital image forgery detection using passive techniques: A survey. Digital Investigation. 10(3): 226-245.

Agarwal, S. & Chand, S., 2018. Image Forgery Detection Using Co-occurrence-Based Texture Operator in Frequency Domain. In Progress in Intelligent Computing Techniques: Theory, Practice, and Applications. Springer, Singapore.: 117-122.

Gautam, S. and Jalal, A.S., 2018. An Image Forgery Detection Approach Based on Camera's Intrinsic Noise Properties. International Journal of Computer Vision and Image Processing. 8(1): 92-101.

Mahdian, B., & Saic, S. 2009. Using noise inconsistencies for blind image forensics. Image and Vision Computing. 27(10): 1497-1503.

Wang, Y., Zhao, Q., Jiang, L., & Shao, Y. 2010. Ultra high throughput implementations for MD5 hash algorithm on FPGA. In High Performance Computing and Applications. Springer, Berlin, Heidelberg. : 433-441.

Khan, S., Khan, M.N. & Iqbal, S. 2013. Bit Position Based Qualitative and Quantitative Analysis of DCT and Spatial Domain Steganography. International Journal of Computer Science Issues (IJCSI). 10(3): 169-173.

Lyu, S., & Farid, H. 2002. Detecting hidden messages using higher-order statistics and support vector machines. In Information Hiding: 340-354.

Shi, Y.Q., Xuan, G., Zou, D., Gao, J., & Yang C. 2005. Steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network. In International Conference on Multimedia and Expo. Amsterdam.: 269-272.

Zou, D., Shi, Y.Q., Su, W., and Xuan, G., 2006. Steganalysis based on Markov model of thresholded prediction-error image. In IEEE International Conference on Multimedia and Expo.: 1365-1368.

Rad, R.M., & Wong, K. 2015. Digital image forgery detection by edge analysis. In IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW).: 19-20.

Kashyap, A., Parmar, R.S., Suresh, B., Agarwal, M., & Gupta, H. 2016. Detection of digital image forgery using wavelet decomposition and outline analysis. In International Conference on Signal Processing and Communication (ICSC).: 187-190.

Khan, S., & Bianchi, T. 2019. Reduced Complexity Image Clustering Based on Camera Fingerprints. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Brighton, United Kingdom.: 2682-2688. IEEE.

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