A Deep Learning Framework for Detecting Digital Image Forgery Using a Hybrid U-Net
Keywords:
Image forgery detection, splicing detection, U-Net, Image Forensics, VGG16.Abstract
To detect and localize picture modifications a hybrid U-Net-based image forgery detection method that merges deep learning with semantic segmentation models is proposed. Our method uses a hybrid U-Net architecture with feature extraction, semantic segmentation, and classification modules. Feature extraction uses the VGG16 model, whereas semantic segmentation uses a modified U-Net model with residual connections. The classification module detects picture modifications using binary classification on a fully linked network. We verified our method on the CASIA2 dataset, which contains 10,000 photos with various image modifications. We tested our strategy using 5-fold cross-validation and compared it to several state-of-the-art methods. Our method outperformed others in accuracy, robustness, and efficiency, showing its promise for identifying image modifications in real-world conditions. Our effective and efficient method for identifying diverse picture modifications with high accuracy and robustness makes a substantial addition to image forgery detection. Digital forensics, picture authentication, and related industries will benefit from the suggested technique, which will make image-based systems more trustworthy.
Downloads
References
Yu Sun, Rongrong Ni and Yao Zhao, “ ET: Edge-Enhanced Transformer for Image Splicing Detection” IEEE Signal Processing Letters, Vol. 29, pp. 1232-1236, 2022.
Kang Hyeon Rhee, “Generation of Novelty Ground Truth Image Using Image Classification and Semantic Segmentation for Copy-Move Forgery Detection” IEEE Access, Vol. 10, pp. 2783 – 2796,
Chengyou Wang , Zhi Zhang , Qianwen Li And Xiao Zhou, “An Image Copy-Move Forgery Detection Method Based on SURF and PCET” IEEE Access, Vol. 7, pp. 170032 – 170047, 2019.
Weijie Gan, Yu Sun, Cihat Eldeniz, Jiaming Liu, Hongyu An,Ulugbek S. Kamilov, “Deep Image Reconstruction Using Unregistered Measurements Without Groundtruth” 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
S. Lyu, X. Pan and X. Zhang, "Exposing region splicing forgeries with blind local noise estimation", Int. J. Comput. Vis., vol. 110, no. 2, pp. 202-221, Nov. 2014.
M. Huh, A. Liu, A. Owens and A. A. Efros, "Fighting fake news: Image splice detection via learned self-consistency", Proc. Eur. Conf. Comput. Vis., pp. 101-117, 2018.
Y. Li and J. Zhou, "Fast and effective image copy-move forgery detection via hierarchical feature point matching", IEEE Trans. Inf. Forensics Security, vol. 14, no. 5, pp. 1307-1322, May 2019.
J.-L. Zhong and C.-M. Pun, "An end-to-end dense-InceptionNet for image copy-move forgery detection", IEEE Trans. Inf. Forensics Security, vol. 15, pp. 2134-2146, 2020.
J. Zhang, Y. Liao, X. Zhu, H. Wang and J. Ding, "A deep learning approach in the discrete cosine transform domain to median filtering forensics", IEEE Signal Process. Lett., vol. 27, pp. 276-280, 2020.
Q. Bammey, R. G. V. Gioi and J.-M. Morel, "An adaptive neural network for unsupervised mosaic consistency analysis in image forensics", Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pp. 14182-14192, 2020.
P. Zhuang, H. Li, S. Tan, B. Li and J. Huang, "Image tampering localization using a dense fully convolutional network", IEEE Trans. Inf. Forensics Secur., vol. 16, pp. 2986-2999, 2021.
Z. Shi, X. Shen, H. Chen and Y. Lyu, "Global semantic consistency network for image manipulation detection", IEEE Signal Process. Lett., vol. 27, pp. 1755-1759, 2020.
M. D. M. Hosseini and M. Kirchner, "Unsupervised image manipulation localization with non-binary label attribution", IEEE Signal Process. Lett., vol. 26, no. 7, pp. 976-980, Jul. 2019.
G. Singh and K. Singh, "Digital image forensic approach based on the second-order statistical analysis of CFA artifacts", Forensic Sci. Int.: Digit. Investigation, vol. 32, 2020.
P. Zhou, X. Han, V. I. Morariu and L. S. Davis, "Learning rich features for image manipulation detection", Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 1053-1061, 2018.
J. Fridrich, D. Soukal and J. Lukáš, "Detection of copy–move forgery in digital images", Proc. Digit. Forensic Res. Workshop, Aug. 2003.
A. C. Popescu and H. Farid, "Exposing digital forgeries by detecting duplicated image regions", 2004.
W. Luo, J. Huang and G. Qiu, "Robust detection of region-duplication forgery in digital image", Proc. 18th Int. Conf. Pattern Recognit. (ICPR), pp. 746-749, Aug. 2006.
G. Li, Q. Wu, D. Tu and S. Sun, "A sorted neighborhood approach for detecting duplicated regions in image forgeries based on DWT and SVD", Proc. IEEE Int. Conf. Multimedia Expo, pp. 1750-1753, Jul. 2007.
B. Mahdian and S. Saic, "Detection of copy–move forgery using a method based on blur moment invariants", Forensic Sci. Int., vol. 171, no. 2, pp. 180-189, 2007.
X. B. Kang and S. M. Wei, "Identifying tampered regions using singular value decomposition in digital image forensics", Proc. Int. Conf. Comput. Sci. Softw. Eng., pp. 926-930, Dec. 2008.
S. Bayram, H. T. Sencar and N. Memon, "An efficient and robust method for detecting copy–move forgery", Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), pp. 1053-1056, Apr. 2009.
J. Wang, G. Liu, H. Li, Y. Dai and Z. Wang, "Detection of image region duplication forgery using model with circle block", Proc. Int. Conf. Multimedia Inf. Netw. Secur. (MINES), pp. 25-29, Nov. 2009.
J. W. Wang, G. J. Liu, Z. Zhang, Y. W. Dai and Z. Q. Wang, "Fast and robust forensics for image region-duplication forgery", Acta Automat. Sinica, vol. 35, no. 12, pp. 1488-1495, 2009.
H. J. Lin, C. W. Wang and Y. T. Kao, "Fast copy–move forgery detection", WSEAS Trans. Signal Process., vol. 5, no. 5, pp. 188-197, 2009.
S. J. Ryu, M. J. Lee and H. K. Lee, "Detection of copy-rotate-move forgery using Zernike moments" in Information Hiding., Berlin, Germany:Springer-Verlag, pp. 51-65, 2010.
S. J. Ryu, M. Kirchner, M. J. Lee and H. K. Lee, "Rotation invariant localization of duplicated image regions based on Zernike moments", IEEE Trans. Inf. Forensics Security, vol. 8, no. 8, pp. 1355-1370, Aug. 2013.
S. Bravo-Solorio and A. K. Nandi, "Exposing duplicated regions affected by reflection rotation and scaling", Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), pp. 1880-1883, May 2011.
T. J. de Carvalho, C. Riess, E. Angelopoulou, H. Pedrini and A. Rocha, "Exposing digital image forgeries by illumination color classification", IEEE Trans. Inf. Forensics Security, vol. 8, no. 7, pp. 1182-1194, Jul. 2013.
Y.-F. Hsu and S.-F. Chang, "Detecting image splicing using geometry invariants and camera characteristics consistency", Proc. IEEE Int. Conf. Multimedia Expo., pp. 549-552, Jul. 2006.
Nist Nimble 2016 Datasets, Jan. 2022, [online] Available: https://www.nist.gov/itl/iad/mig/nimble-challenge-2017-evaluation/.
J. Dong, W. Wang and T. Tan, "CASIA image tampering detection evaluation database", Proc. IEEE China Summit Int. Conf. Signal Inf. Process., pp. 422-426, Jul. 2013.
Q. Gao and X. Wu, "Real-time deep image retouching based on learnt semantics dependent global transforms", IEEE Trans. Image Process., vol. 30, pp. 7378-7390, 2021.
J. He, Y. Liu, Y. Qiao and C. Dong, "Conditional sequential modulation for efficient global image retouching", Proc. Eur. Conf. Comput. Vis., pp. 679-695, Sep. 2020.
H. Zhao, T. Wei, W. Zhou, W. Zhang, D. Chen and N. Yu, "Multi-attentional deepfake detection", Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 2185-2194, Jun. 2021.
P. Wang et al., "ADT: Anti-deepfake transformer", Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), pp. 1903-2899, May 2022.
X. Dong et al., "Protecting celebrities from DeepFake with identity consistency transformer", Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 9468-9478, Jun. 2022.
Q. Gu, S. Chen, T. Yao, Y. Chen, S. Ding and R. Yi, "Exploiting fine-grained face forgery clues via progressive enhancement learning", Proc. AAAI Conf. Artif. Intell., pp. 735-743, 2022.
W. Zhuang et al., "UIA-ViT: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection", Proc. Eur. Conf. Comput. Vis., pp. 391-407, Oct. 2022.
K. Sun et al., "An information theoretic approach for attention-driven face forgery detection", Proc. Eur. Conf. Comput. Vis., pp. 111-127, Oct. 2022.
H. Farid, "Image forgery detection", IEEE Signal Processing Magazine, vol. 26, no. 2, pp. 16-25, 2009.
M. Kumar and S. Srivastava, "Image forgery detection based on physics and pixels: A study", Australian Journal of Forensic Sciences, vol. 51, no. 2, pp. 119-134, 2019.
M. Asikuzzaman and M. R. Pickering, "An overview of digital video watermarking", IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 9, pp. 2131-2153, 2018.
K. Jung, "A survey of reversible data hiding methods in dual images", IETE Technical Review, vol. 33, no. 4, pp. 441-452, 2016.
A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, A. A. Bharath, Generative adversarial networks: An overview, IEEE Signal Processing Magazine 35 (1) (2018) 53–65.
S. Ahirwar and A. Pandey, "Digital Image Forgery Detection using Convolutional Neural Network (CNN): A Survey," 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal, India, 2024, pp. 1-6, doi: 10.1109/SCEECS61402.2024.10481917.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
