基于CycleGAN的图像隐私保护

doi:10.3969/j.issn.0255-8297.2023.02.004

应用科学学报 ›› 2023, Vol. 41 ›› Issue (2): 228-239.doi: 10.3969/j.issn.0255-8297.2023.02.004

基于CycleGAN的图像隐私保护

谢艺艺¹, 张玉书¹, 赵若宇¹, 温文², 周玉倩¹

1. 南京航空航天大学计算机科学与技术学院, 江苏南京 211106;
2. 江西财经大学信息管理学院, 江西南昌 330013

收稿日期:2022-10-29 出版日期:2023-03-31 发布日期:2023-03-29
通信作者: 张玉书,教授,研究方向为多媒体安全、区块链及其应用。E-mail:yushu@nuaa.edu.cn E-mail:yushu@nuaa.edu.cn
基金资助:
国家重点研发计划（No.2021YFB3100400）资助

Image Privacy Protection Based on Cycle-Consistent Generative Adversarial Networks

XIE Yiyi¹, ZHANG Yushu¹, ZHAO Ruoyu¹, WEN Wenying², ZHOU Yuqian¹

1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China;
2. School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330013, Jiangxi, China

Received:2022-10-29 Online:2023-03-31 Published:2023-03-29

摘要/Abstract

摘要： 社交媒体和云平台为图像的传播和存储带来了便利，但同时也引起了人们对于图像隐私的担忧。因此，需要采取一定的措施去保护图像的隐私，以防止隐私被窃取和非法使用。基于上述目标，本文提出了基于循环对抗网络（cycle-consistent generative adversarialnetworks,CycleGAN）的图像隐私保护。为了在图像隐私保护中兼顾可用性，该方法先用图像分割和CycleGAN组合，选择出不同的分割系数来辅助生成不同程度的隐私保护图像。然后利用可逆信息隐藏对生成的隐私保护图像进行信息的嵌入，从而阻止非法使用者在图像重构中提取隐私信息，进而保证了整个过程图像隐私保护和可用性的平衡。本文用PIPA数据集对该方法进行训练和测试，采用峰值信噪比和结构相似性指数作为客观指标对隐私保护的图像进行评估。实验结果表明，本方案在图像隐私保护和可用性两方面都优于其他对比方案。

关键词: 图像隐私保护, 图像分割, CycleGAN模型, 可逆信息隐藏, 图像重构

Abstract: Social media and cloud computing have facilitated the distribution and storage of images. Meanwhile, concerns about image privacy have been raised. It is crucial to protect image privacy from privacy violation and illegal use. Motivated by this, an image privacy protection method based on cycle-consistent generative adversarial networks (CycleGAN) is proposed in this paper. Considering the usability in image privacy protection, the method first combines image segmentation with CycleGAN to select different segmentation coefficients to generate images with different degrees of privacy protection. Then reversible information hiding is used to embed information in the generated privacy preserving image, so as to prevent illegal users from reversing the image. Thus, a balance is achieved between image privacy protection and usability. The proposed method is trained and tested using PIPA dataset, using peak signal to noise ratio and structural similarity index are used as performance metrics to evaluate the privacy-preserving images. Experimental results show that the proposed method outperforms other schemes in both image privacy preservation and usability.

Key words: image privacy protection, image segmentation, cycle-consistent generative adversarial networks (CycleGAN), reversible data hiding, image reconstruction

中图分类号:

TP389.1

谢艺艺, 张玉书, 赵若宇, 温文媖, 周玉倩. 基于CycleGAN的图像隐私保护[J]. 应用科学学报, 2023, 41(2): 228-239.

XIE Yiyi, ZHANG Yushu, ZHAO Ruoyu, WEN Wenying, ZHOU Yuqian. Image Privacy Protection Based on Cycle-Consistent Generative Adversarial Networks[J]. Journal of Applied Sciences, 2023, 41(2): 228-239.

参考文献

[1] Mcpherson R, Shokri R, Shmatikov V. Defeating image obfuscation with deep learning[DB/OL]. 2016[2022-10-29]. https://arxiv.org/abs/1609.00408.
[2] Raval N, Machanavajjhala A, Cox L P. Protecting visual secrets using adversarial nets[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017:1329-1332.
[3] Nandakumar K, Ratha N, Pankanti S, et al. Towards deep neural network training on encrypted data[C]//2019 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019:40-48.
[4] 张赛男, 李千目. 彩色图像隐私部分自动标定及加密[J]. 南京理工大学学报, 2022, 46(4):395-405. Zhang S N, Li Q M. Automatic calibration and encryption of privacy part of color image[J]. Journal of Nanjing University of Science and Technology, 2022, 46(4):395-405. (in Chinese)
[5] Newton E M, Sweeney L, Malin B. Preserving privacy by de-identifying face images[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(2):232-243.
[6] Gross R, Airoldi E, Malin B, et al. Integrating utility into face de-identification[C]//International Workshop on Privacy Enhancing Technologies. Springer, 2005:227-242.
[7] Meng L, Shenoy A. Retaining expression on de-identified faces[C]//International Conference on Speech and Computer. Springer, 2017:651-661.
[8] Tabassum A, Erbad A, Lebda W, et al. FEDGAN-IDS:privacy-preserving IDS using GAN and federated learning[J]. Computer Communications, 2022, 192:299-310.
[9] 葛瑞. 基于生成对抗网络的图像隐私保护方法研究[D]. 西安:西安电子科技大学, 2020.
[10] Ren Z Z, Lee Y J, Ryoo M S. Learning to anonymize faces for privacy preserving action detection[C]//European Conference on Computer Vision, 2018:639-655.
[11] Wu Y F, Yang F, Xu Y, et al. Privacy-protective-GAN for privacy preserving face deidentification[J]. Journal of Computer Science and Technology, 2019, 34(1):47-60.
[12] 毛典辉, 李子沁, 蔡强, 等. 基于DCGAN反馈的深度差分隐私保护方法[J]. 北京工业大学学报, 2018(6):870-877. Mao D H, Li Z Q, Cai Q, et al. Tickling deep differential privacy protection method based on DCGAN[J]. Journal of Beijing University of Technology, 2018(6):870-877. (in Chinese)
[13] 周莉莉, 姜枫. 图像分割方法综述研究[J]. 计算机应用研究, 2017, 34(7):1921-1928. Zhou L L, Jiang F. Survey on image segmentation methods[J]. Application Research of Computers, 2017, 34(7):1921-1928. (in Chinese)
[14] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4):640-651.
[15] Borse S, Wang Y, Zhang Y Z, et al. InverseForm:a loss function for structured boundaryaware segmentation[C]//2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021:5897-5907.
[16] Felzenszwalb P F, Huttenlocher D P. Efficient graph-based image segmentation[J]. International Journal of Computer Vision, 2004, 59(2):167-181.
[17] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[J]. Advances in Neural Information Processing Systems, 2014:2672-2680.
[18] Men Y F, Mao Y M, Jiang Y N, et al. Controllable person image synthesis with attributedecomposed GAN[C]//2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020:5083-5092.
[19] Zhu J Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//2017 IEEE International Conference on Computer Vision, 2017:2242-2251.
[20] Isola P, Zhu J Y, Zhou T H, et al. Image-to-image translation with conditional adversarial networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017:5967-5976.
[21] Puteaux P, Ong S Y, Wong K S, et al. A survey of reversible data hiding in encrypted images-the first 12 years[J]. Journal of Visual Communication and Image Representation, 2021, 77:103085.
[22] Abadi M, Andersen D G. Learning to protect communications with adversarial neural cryptography[DB/OL]. 2016[2022-10-29]. https://arxiv.org/abs/1610.06918.
[23] Chu C, Zhmoginov A, Sandler M. Cyclegan, a master of steganography[DB/OL]. 2017[2022-10-29]. https://arxiv.org/abs/1712.02950.
[24] Wu H, Tian X J, Li M H, et al. PECAM:privacy-enhanced video streaming and analytics via securely-reversible transformation[C]//27th Annual International Conference on Mobile Computing and Networking, 2021:229-241.
[25] Kim H J, Sachnev V, Shi Y Q, et al. A novel difference expansion transform for reversible data embedding[J]. IEEE Transactions on Information Forensics and Security, 2008, 3(3):456-465.
[26] Zhang N, Paluri M, Taigman Y, et al. Beyond frontal faces:improving Person Recognition using multiple cues[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015:4804-4813.

基于CycleGAN的图像隐私保护

Image Privacy Protection Based on Cycle-Consistent Generative Adversarial Networks

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	翁科, 秦健豪, 宋天然, 石慧. 基于双域联合编码和秘密共享的密文域可逆信息隐藏[J]. 应用科学学报, 2023, 41(2): 197-217.
[2]	张翔宇, 栗风永, 秦川. 基于块分类的密文域多重嵌入可逆信息隐藏算法[J]. 应用科学学报, 2023, 41(2): 272-283.
[3]	袁文杰, 田金鹏, 杨洁. 基于超深残差通道注意力网络的图像压缩感知重构[J]. 应用科学学报, 2022, 40(6): 887-895.
[4]	马飞虎, 曾聪, 金依辰, 孙翠羽, 陈华鹏. 一种基于属性加权朴素贝叶斯算法的OTSU图像分割方法[J]. 应用科学学报, 2022, 40(2): 224-232.
[5]	何玉芬, 汤进, 殷赵霞. 基于多对非对称直方图修改的可逆信息隐藏[J]. 应用科学学报, 2022, 40(2): 253-265.
[6]	杨阳, 李欣然, 胡锦川. 基于块重构的加密域可逆信息隐藏[J]. 应用科学学报, 2021, 39(6): 906-922.
[7]	孙容海, 施林甫, 俞春强, 劳欢, 唐振军. 面向密文图像信息隐藏的随机插值方法[J]. 应用科学学报, 2020, 38(3): 419-430.
[8]	屈凌峰, 陈帆, 和红杰, 袁源. 基于位平面-块置乱的图像加密算法安全性分析[J]. 应用科学学报, 2019, 37(5): 631-642.
[9]	王俊祥, 毛宁雄, 赵怡, 王春桃. 面向全容量的高性能彩色图像可逆信息隐藏算法[J]. 应用科学学报, 2019, 37(5): 744-760.
[10]	何志红, 秦川, 周青. 大容量的密文域图像可逆信息隐藏算法[J]. 应用科学学报, 2018, 36(4): 611-627.
[11]	黄丹, 程思锦, 黄方军. 基于频段选择的JPEG图像可逆信息隐藏[J]. 应用科学学报, 2018, 36(2): 209-219.
[12]	陈艳, 俞春强, 侯晓杰, 张显全, 唐振军, 何南. 基于曲面插值的加密图像可逆信息隐藏算法[J]. 应用科学学报, 2018, 36(2): 220-236.
[13]	刘小凯, 姚恒, 秦川. 基于图像块分类阈值优化的改进可逆图像伪装[J]. 应用科学学报, 2018, 36(2): 237-246.
[14]	王晓雨, 马宾, 李健, 施云庆. 基于多元线性回归的自适应图像可逆信息隐藏误差预测算法[J]. 应用科学学报, 2018, 36(2): 362-370.
[15]	闫兵, 柏森, 阳溢, 季晓勇. 改进的PVO-k可逆信息隐藏算法[J]. 应用科学学报, 2016, 34(5): 605-615.