When Perceptual Authentication Hashing Meets Neural Architecture Search
Pages 8975 - 8983
Abstract
In recent years, many perceptual authentication hashing schemes have been proposed, especially for image content authentication. However, most of the schemes directly use the dataset of image processing during model training and evaluation, which is actually unreasonable due to the task difference. In this paper, we first propose a specialized dataset for perceptual authentication hashing of images (PAHI), and the image content-preserving manipulations used in this dataset are richer and more in line with realistic scenarios. Then, in order to achieve satisfactory perceptual robustness and discrimination capability of PAHI, we exploit the continuous neural architecture search (NAS) on the channel number and stack depth of the ConvNeXt architecture, and obtain two PAHI architectures i.e., NASRes and NASCoNt. The former has better overall performance, while the latter is better for some special manipulations such as image cropping and background overlap. Experimental results demonstrate that our architectures both can achieve competitive results compared with SOTA schemes, and the AUC areas are increased by 1.6 (NASCoNt) and 1.7 (NASRes), respectively.
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References
[1]
Sani M. Abdullahi and Hongxia Wang. 2018. Fourier-Mellin Transform and Fractal Coding for Secure and Robust Fingerprint Image Hashing. In 15th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2018, Auckland, New Zealand, November 27-30, 2018. IEEE, 1--7. https://doi.org/10.1109/AVSS.2018.8639359
[2]
Mouna Bedoui, Belgacem Bouallegue, Abdelmoty M. Ahmed, Belgacem Hamdi, Mohsen Machhout, Mahmoud, and Mahmoud Khattab. 2023. A Secure Hardware Implementation for Elliptic Curve Digital Signature Algorithm. Comput. Syst. Sci. Eng., Vol. 44, 3 (2023), 2177--2193. https://doi.org/10.32604/csse.2023.026516
[3]
Tom B. Brown, Benjamin Mann, and Nick Ryder. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.). https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html
[4]
Navid Danapur, Sakineh Asghari Aghjeh Dizaj, and Vahid Rostami. 2020. An efficient image retrieval based on an integration of HSV, RLBP, and CENTRIST features using ensemble classifier learning. Multim. Tools Appl., Vol. 79, 33--34 (2020), 24463--24486. https://doi.org/10.1007/s11042-020-09109-9
[5]
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA. IEEE Computer Society, 248--255. https://doi.org/10.1109/CVPR.2009.5206848
[6]
Ling Du, Anthony T. S. Ho, and Runmin Cong. 2020. Perceptual hashing for image authentication: A survey. Signal Process. Image Commun., Vol. 81 (2020). https://doi.org/10.1016/j.image.2019.115713
[7]
Kalaiarasi Governor, Padmavathy Ramanujam, Suja Cherukullapurath Mana, and Geetha Perumal. 2023. Near duplicate detection of images with area and proposed pixel-based feature extraction. Concurr. Comput. Pract. Exp., Vol. 35, 2 (2023). https://doi.org/10.1002/cpe.7477
[8]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016. IEEE Computer Society, 770--778. https://doi.org/10.1109/CVPR.2016.90
[9]
Xin He, Kaiyong Zhao, and Xiaowen Chu. 2021. AutoML: A survey of the state-of-the-art. Knowl. Based Syst., Vol. 212 (2021), 106622. https://doi.org/10.1016/j.knosys.2020.106622
[10]
Mehran Kafai, Kave Eshghi, and Bir Bhanu. 2014. Discrete Cosine Transform Locality-Sensitive Hashes for Face Retrieval. IEEE Trans. Multim., Vol. 16, 4 (2014), 1090--1103. https://doi.org/10.1109/TMM.2014.2305633
[11]
Hyunwoo Kim, SungRyull Sohn, and Junmo Kim. 2019. Revisiting Gist-PCA Hashing for Near Duplicate Image Detection. J. Signal Process. Syst., Vol. 91, 6 (2019), 575--586. https://doi.org/10.1007/s11265-018-1360-0
[12]
Suleyman Serdar Kozat, Ramarathnam Venkatesan, and Mehmet Kivanç Mihçak. 2004. Robust perceptual image hashing via matrix invariants. In Proceedings of the 2004 International Conference on Image Processing, ICIP 2004, Singapore, October 24-27, 2004. IEEE, 3443--3446. https://doi.org/10.1109/ICIP.2004.1421855
[13]
Pingyuan Li, Xiaoguang Yuan, and Suiping Jiang. 2021. Image Hashing Based on SIFT Features. In CSSE 2021: 2021 4th International Conference on Computer Science and Software Engineering, Singapore, October 22 - 24, 2021. ACM, 253--256. https://doi.org/10.1145/3494885.3494930
[14]
Yuenan Li, Dongdong Wang, and Linlin Tang. 2020. Robust and Secure Image Fingerprinting Learned by Neural Network. IEEE Trans. Circuits Syst. Video Technol., Vol. 30, 2 (2020), 362--375. https://doi.org/10.1109/TCSVT.2019.2890966
[15]
Kevin Lin, Huei-Fang Yang, Jen-Hao Hsiao, and Chu-Song Chen. 2015. Deep learning of binary hash codes for fast image retrieval. In 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2015, Boston, MA, USA, June 7-12, 2015. IEEE Computer Society, 27--35. https://doi.org/10.1109/CVPRW.2015.7301269
[16]
Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2019. DARTS: Differentiable Architecture Search. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net. https://openreview.net/forum?id=S1eYHoC5FX
[17]
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. 2022. A ConvNet for the 2020s. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022. IEEE, 11966--11976. https://doi.org/10.1109/CVPR52688.2022.01167
[18]
Ilya Loshchilov and Frank Hutter. 2017. SGDR: Stochastic Gradient Descent with Warm Restarts. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=Skq89Scxx
[19]
Xudong Lv and Z. Jane Wang. 2009. An Extended Image Hashing Concept: Content-Based Fingerprinting Using FJLT. EURASIP J. Inf. Secur., Vol. 2009 (2009). https://doi.org/10.1155/2009/859859
[20]
Vishal Monga and Mehmet Kivanç Mihçak. 2007. Robust and Secure Image Hashing via Non-Negative Matrix Factorizations. IEEE Trans. Inf. Forensics Secur., Vol. 2, 3--1 (2007), 376--390. https://doi.org/10.1109/TIFS.2007.902670
[21]
Junlin Ouyang, Xingzi Wen, Jianxun Liu, and Jinjun Chen. 2016. Robust Hashing Based on Quaternion Zernike Moments for Image Authentication. ACM Trans. Multim. Comput. Commun. Appl., Vol. 12, 4s (2016), 63:1--63:13. https://doi.org/10.1145/2978572
[22]
Ravi Parashivamurthy, Chikkaguddaiah Naveena, and Yeliyur Hanumathiah Sharath Kumar. 2020. SIFT and HOG features for the retrieval of ancient Kannada epigraphs. IET Image Process., Vol. 14, 17 (2020), 4657--4662. https://doi.org/10.1049/iet-ipr.2020.0715
[23]
Varsha Patil and Tanuja K. Sarode. 2019. Image Hashing Using DWT-CSLBP. J. Comput., Vol. 14, 3 (2019), 210--222. https://doi.org/10.17706/jcp.14.3.210-222
[24]
Chuan Qin, Xueqin Chen, Xiangyang Luo, Xinpeng Zhang, and Xingming Sun. 2018. Perceptual image hashing via dual-cross pattern encoding and salient structure detection. Inf. Sci., Vol. 423 (2018), 284--302. https://doi.org/10.1016/j.ins.2017.09.060
[25]
Chuan Qin, Enli Liu, Guorui Feng, and Xinpeng Zhang. 2021. Perceptual Image Hashing for Content Authentication Based on Convolutional Neural Network With Multiple Constraints. IEEE Trans. Circuits Syst. Video Technol., Vol. 31, 11 (2021), 4523--4537. https://doi.org/10.1109/TCSVT.2020.3047142
[26]
Pulkit Rathi, Saumya Bhadauria, and Sugandha Rathi. 2022. Watermarking of Deep Recurrent Neural Network Using Adversarial Examples to Protect Intellectual Property. Appl. Artif. Intell., Vol. 36, 1 (2022). https://doi.org/10.1080/08839514.2021.2008613
[27]
Gerald Schaefer and Michal Stich. 2004. UCID: an uncompressed color image database. In Storage and Retrieval Methods and Applications for Multimedia 2004, San Jose, CA, USA, January 20, 2004 (SPIE Proceedings, Vol. 5307), Minerva M. Yeung, Rainer Lienhart, and Chung-Sheng Li (Eds.). SPIE, 472--480. https://doi.org/10.1117/12.525375
[28]
Xiaohan Sun and Jiting Zhou. 2022. Deep Perceptual Hash Based on Hash Center for Image Copyright Protection. IEEE Access, Vol. 10 (2022), 120551--120562. https://doi.org/10.1109/ACCESS.2022.3221980
[29]
Zhenjun Tang, Xianquan Zhang, Xianxian Li, and Shichao Zhang. 2016. Robust Image Hashing With Ring Partition and Invariant Vector Distance. IEEE Trans. Inf. Forensics Secur., Vol. 11, 1 (2016), 200--214. https://doi.org/10.1109/TIFS.2015.2485163
[30]
Darshana Upadhyay, Nupur Gaikwad, Marzia Zaman, and Srinivas Sampalli. 2022. Investigating the Avalanche Effect of Various Cryptographically Secure Hash Functions and Hash-Based Applications. IEEE Access, Vol. 10 (2022), 112472--112486. https://doi.org/10.1109/ACCESS.2022.3215778
[31]
Xiaofeng Wang, Xiaorui Zhou, Qian Zhang, Bingchao Xu, and Jianru Xue. 2020. Image alignment based perceptual image hash for content authentication. Signal Process. Image Commun., Vol. 80 (2020). https://doi.org/10.1016/j.image.2019.115642
[32]
Susila Windarta, Suryadi, Kalamullah Ramli, Bernardi Pranggono, and Teddy Surya Gunawan. 2022. Lightweight Cryptographic Hash Functions: Design Trends, Comparative Study, and Future Directions. IEEE Access, Vol. 10 (2022), 82272--82294. https://doi.org/10.1109/ACCESS.2022.3195572
[33]
Cai-Ping Yan, Chi-Man Pun, and Xiaochen Yuan. 2016. Multi-scale image hashing using adaptive local feature extraction for robust tampering detection. Signal Process., Vol. 121 (2016), 1--16. https://doi.org/10.1016/j.sigpro.2015.10.027
[34]
Hengfu Yang, Jianping Yin, and Ying Yang. 2019. Robust Image Hashing Scheme Based on Low-Rank Decomposition and Path Integral LBP. IEEE Access, Vol. 7 (2019), 51656--51664. https://doi.org/10.1109/ACCESS.2019.2911207
[35]
Jiaqi Yang, Xuequan Lu, and Wenzhi Chen. 2022. A robust scheme for copy detection of 3D object point clouds. Neurocomputing, Vol. 510 (2022), 181--192. https://doi.org/10.1016/j.neucom.2022.09.008
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October 2023
9913 pages
ISBN:9798400701085
DOI:10.1145/3581783
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Published: 27 October 2023
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