Image Hashing Robust Against Cropping and Rotation

Authors

  • Martin Steinebach Fraunhofer SIT/ATHENE, Germany https://orcid.org/0000-0002-0240-0388
  • Tiberius Berwanger TU Darmstadt/ATHENE, Germany
  • Huajian Liu Fraunhofer SIT/ATHENE, Germany

DOI:

https://doi.org/10.13052/jcsm2245-1439.1221

Keywords:

Image identification, robust hashing, feature-based hashing, rotation, segmentation

Abstract

Image recognition is an important mechanism used in various scenarios. In the context of multimedia forensics, its most significant task is to automatically detect already known child and adolescent pornography in a large set of images. When fighting disinformation, it is used to identify images taken out of context or image montages. For this purpose, numerous methods based on robust hashing and feature extraction are already known, and recently also supported by machine learning. However, in general, these methods are either only partially robust to changes such as rotation and pruning, or they require a large amount of data and computation. We present a method based on a simple block hash that is efficient to compute and memory efficient. To be robust against cropping and rotation, we combine the method with image segmentation and a method to normalize the rotation of the objects. Our evaluation shows that the method produces results comparable to much more complex approaches, but requires fewer resources.

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Author Biographies

Martin Steinebach, Fraunhofer SIT/ATHENE, Germany

Martin Steinebach is the manager of the Media Security and IT Forensics division at Fraunhofer SIT. From 2003 to 2007 he was the manager of the Media Security in IT division at Fraunhofer IPSI. He studied computer science at the Technical University of Darmstadt and finished his diploma thesis on copyright protection for digital audio in 1999. In 2003 he received his PhD at the Technical University of Darmstadt for this work on digital audio watermarking. In 2016 he became honorary professor at the TU Darmstadt. He gives lectures on Multimedia Security as well as Civil Security. He is Principle Investigator at ATHENE and represents IT Forensics and AI security. Before he was Principle Investigator at CASED with the topics Multimedia Security and IT Forensics. In 2012 his work on robust image hashing for detection of child pornography reached the second rank “Deutscher ITSicherheitspreis”, an award funded by Host Görtz.

Huajian Liu, Fraunhofer SIT/ATHENE, Germany

Huajian Liu received his B.S. and M.S. degrees in electronic engineering from Dalian University of Technology, China, in 1999 and 2002, respectively, and his Ph.D. degree in computer science from Technical University Darmstadt, Germany, in 2008. He is currently a senior research scientist at Fraunhofer Institute for Secure Information Technology (SIT). His major research interests include information security, digital watermarking, robust hashing and digital forensics.

References

Yali Amit, Pedro Felzenszwalb, and Ross Girshick. Object detection. In Computer Vision, pages 1–9. Springer International Publishing, Cham, 2020.

Ali Ismail Awad and Mahmoud Hassaballah, editors. Image Feature Detectors and Descriptors: Foundations and Applications, volume 630 of Studies in Computational Intelligence. Springer International Publishing, Cham and s.l., 1st ed. 2016 edition, 2016.

Herbert Bay, Tinne Tuytelaars, and Luc van Gool. Surf: Speeded up robust features. In Aleš Leonardis, Horst Bischof, and Axel Pinz, editors, Computer Vision – ECCV 2006, volume 3951 of Lecture Notes in Computer Science, pages 404–417. Springer Berlin Heidelberg, Berlin, Heidelberg, 2006.

S. Bhattacharjee and M. Kutter. Compression tolerant image authentication. In Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269), pages 435–439. IEEE Comput. Soc, 1998.

Bert De Brabandere, Davy Neven, and Luc van Gool. Semantic instance segmentation with a discriminative loss function.

Uwe Breidenbach, Martin Steinebach, and Huajian Liu. Privacy-enhanced robust image hashing with bloom filters. In Melanie Volkamer and Christian Wressnegger, editors, ARES 2020: The 15th International Conference on Availability, Reliability and Security, Virtual Event, Ireland, August 25–28, 2020, pages 56:1–56:10. ACM, 2020.

Hongliang Cai, Huajian Liu, Martin Steinebach, and Xiaojing Wang. A roi-based self-embedding method with high recovery capability. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1722–1726. IEEE, 2015.

Zhangjie Cao, Mingsheng Long, Jianmin Wang, and Philip S Yu. Hashnet: Deep learning to hash by continuation. In Proceedings of the IEEE international conference on computer vision, pages 5608–5617, 2017.

Hao Chen, Kunyang Sun, Zhi Tian, Chunhua Shen, Yongming Huang, and Youliang Yan. Blendmask: Top-down meets bottom-up for instance segmentation.

Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. Rethinking atrous convolution for semantic image segmentation.

Sujan Chowdhury, Brijesh Verma, Mary Tom, and Mengjie Zhang. Pixel characteristics based feature extraction approach for roadside object detection. In 2015 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE, 2015.

Cityscape. Dataset overview – cityscapes dataset, 2021-09-08.

B. Coskun and B. Sankur. Video isaretlerinin algisal dayanikli kiyimi – robust videohas extraction. In Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, 2004, pages 292–295. IEEE, 2004.

Andrea Drmic, Marin Silic, Goran Delac, Klemo Vladimir, and Adrian S. Kurdija. Evaluating robustness of perceptual image hashing algorithms. In 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pages 995–1000. IEEE, 2017.

Ling Du, Anthony T.S. Ho, and Runmin Cong. Perceptual hashing for image authentication: A survey. Signal Processing: Image Communication, 81:115713, 2020.

F. Lefèbvre, B. Macq, and J. Legat. Rash: Radon soft hash algorithm. In 2002 11th European Signal Processing Conference, pages 1–4, 2002.

Raphael Antonius Frick, Huajian Liu, and Martin Steinebach. Detecting double compression and splicing using benfords first digit law. In Proceedings of the 15th International Conference on Availability, Reliability and Security, pages 1–9, 2020.

Abdul Mueed Hafiz and Ghulam Mohiuddin Bhat. A survey on instance segmentation: state of the art. International Journal of Multimedia Information Retrieval, 9(3):171–189, 2020.

Hany Farid. Photo tampering throughout history.

Mahmoud Hassaballah, Aly Amin Abdelmgeid, and Hammam A Alshazly. Image features detection, description and matching. In Image Feature Detectors and Descriptors, pages 11–45. Springer, 2016.

Chun-Rong Huang, Chu-Song Chen, and Pau-Choo Chung. Contrast context histogram—an efficient discriminating local descriptor for object recognition and image matching. Pattern Recognition, 41(10):3071–3077, 2008.

Stefan Katzenbeisser, Huajian Liu, and Martin Steinebach. Challenges and solutions in multimedia document authentication. In Handbook of Research on Computational Forensics, Digital Crime, and Investigation: Methods and Solutions, pages 155–175. IGI Global, 2010.

Sultan Daud Khan and Habib Ullah. A survey of advances in vision-based vehicle re-identification. Computer Vision and Image Understanding, 182:50–63, 2019.

Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, and Piotr Dollár. Panoptic segmentation.

Anton Kornilov and Ilia Safonov. An overview of watershed algorithm implementations in open source libraries. Journal of Imaging, 4(10):123, 2018.

Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár. Microsoft coco: Common objects in context.

Huajian Liu and Martin Steinebach. Digital watermarking for image authentication with localization. In 2006 International Conference on Image Processing, pages 1973–1976. IEEE, 2006.

Yiding Liu, Siyu Yang, Bin Li, Wengang Zhou, Jizheng Xu, Houqiang Li, and Yan Lu. Affinity derivation and graph merge for instance segmentation.

David G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91–110, 2004.

Shervin Minaee, Yuri Y. Boykov, Fatih Porikli, Antonio J. Plaza, Nasser Kehtarnavaz, and Demetri Terzopoulos. Image segmentation using deep learning: A survey. IEEE transactions on pattern analysis and machine intelligence, PP, 2021.

Dat Tien Nguyen, Firoj Alam, Ferda Ofli, and Muhammad Imran. Automatic image filtering on social networks using deep learning and perceptual hashing during crises.

Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection.

Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks.

S. Beucher. Use of watersheds in contour detection, 1979.

Scikit. Regionprops funktion.

Martin Steinebach. Robust hashing for efficient forensic analysis of image sets. In Pavel Gladyshev and Marcus K. Rogers, editors, Digital Forensics and Cyber Crime, volume 88 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pages 180–187. Springer Berlin Heidelberg, Berlin, Heidelberg, 2012.

Martin Steinebach, Tiberius Berwanger, and Huajian Liu. Image montage detection based on image segmentation and robust hashing techniques. Electronic Imaging, 34:1–6, 2022.

Martin Steinebach, Tiberius Berwanger, and Huajian Liu. Towards image hashing robust against cropping and rotation. In Proceedings of the 17th International Conference on Availability, Reliability and Security, pages 1–7, 2022.

Martin Steinebach, Karol Gotkowski, and Hujian Liu. Fake news detection by image montage recognition. In Proceedings of the 14th International Conference on Availability, Reliability and Security, pages 1–9, New York, NY, USA, 2019. ACM.

Martin Steinebach, Sebastian Jörg, and Huajian Liu. Checking the integrity of images with signed thumbnail images. Electronic Imaging, 2020(4):118–1, 2020.

Martin Steinebach, Huajian Liu, and York Yannikos. Forbild: Efficient robust image hashing. In Media Watermarking, Security, and Forensics 2012, volume 8303, page 83030O. International Society for Optics and Photonics, 2012.

Martin Steinebach, Huajian Liu, and York Yannikos. Efficient cropping-resistant robust image hashing. In 2014 Ninth International Conference on Availability, Reliability and Security, pages 579–585. IEEE, 2014.

Martin Steinebach, Huajian Liu, and York Yannikos. Facehash: Face detection and robust hashing. In Pavel Gladyshev, Andrew Marrington, and Ibrahim Baggili, editors, Digital Forensics and Cyber Crime, volume 132 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pages 102–115. Springer International Publishing, Cham, 2014.

Shaharyar Ahmed Khan Tareen and Zahra Saleem. A comparative analysis of sift, surf, kaze, akaze, orb, and brisk. In 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pages 1–10. IEEE, 2018.

Theodore Tsesmelis and OpenCV. Introduction to principal component analysis (pca).

Stefan Thiemert, Hichem Sahbi, and Martin Steinebach. Using entropy for image and video authentication watermarks. In Security, Steganography, and Watermarking of Multimedia Contents VIII, volume 6072, page 607218. International Society for Optics and Photonics, 2006.

K. K. Thyagharajan and G. Kalaiarasi. A review on near-duplicate detection of images using computer vision techniques. Archives of Computational Methods in Engineering, 28(3):897–916, 2021.

Zhi Tian, Chunhua Shen, Hao Chen, and Tong He. Fcos: Fully convolutional one-stage object detection.

Yunchao Wei, Xiaodan Liang, Yunpeng Chen, Zequn Jie, Yanhui Xiao, Yao Zhao, and Shuicheng Yan. Learning to segment with image-level annotations. Pattern Recognition, 59:234–244, 2016.

Bian Yang, Fan Gu, and Xiamu Niu. Block mean value based image perceptual hashing. In 2006 International Conference on Intelligent Information Hiding and Multimedia, pages 167–172. IEEE, 2006.

Bian Yang, Fan Gu, and Xiamu Niu. Block mean value based image perceptual hashing. In 2006 International Conference on Intelligent Information Hiding and Multimedia, pages 167–172. IEEE, 2006.

Mang Ye, Jianbing Shen, Gaojie Lin, Tao Xiang, Ling Shao, and Steven C. H. Hoi. Deep learning for person re-identification: A survey and outlook. IEEE transactions on pattern analysis and machine intelligence, PP, 2021.

Christoph Zauner, Martin Steinebach, and Eckehard Hermann. Rihamark: perceptual image hash benchmarking. In Nasir D. Memon, Jana Dittmann, Adnan M. Alattar, and Edward J. Delp III, editors, Media Watermarking, Security, and Forensics III, SPIE Proceedings, page 78800X. SPIE, 2011.

Fanfeng Zeng, Shengda Hu, and Ke Xiao. Deep hash for latent image retrieval. Multimedia Tools and Applications, 78(22):32419–32435, 2019.

Shiliang Zhang, Qi Tian, Ke Lu, Qingming Huang, and Wen Gao. Edge-sift: discriminative binary descriptor for scalable partial-duplicate mobile search. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 22(7):2889–2902, 2013.

Jiaojiao Zhao and Cees G. M. Snoek. Liftpool: Bidirectional convnet pooling.

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Published

2023-05-03

How to Cite

1.
Steinebach M, Berwanger T, Liu H. Image Hashing Robust Against Cropping and Rotation. JCSANDM [Internet]. 2023 May 3 [cited 2024 Apr. 26];12(02):129-60. Available from: https://journals.riverpublishers.com/index.php/JCSANDM/article/view/19049

Issue

2023: Vol 12 Iss 2

Section

ARES 2022 Workshops

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