结合可逆神经网络和逆梯度注意力的抗屏摄攻击水印方法

李谢华; 娄芹; 杨俊雪; 廖鑫

doi:10.11999/JEIT230953

结合可逆神经网络和逆梯度注意力的抗屏摄攻击水印方法

doi: 10.11999/JEIT230953

湖南大学信息科学与工程学院长沙 410082

基金项目: 国家自然科学基金(U22A2030, 61972142)，湖南省自然科学基金(2021JJ30140)，湖南省杰出青年科学基金(2024JJ2025)，长沙市科技重大专项经费(kh2205033)

详细信息

作者简介:
李谢华：女，助理教授，研究方向为大数据安全、云计算与存储系统安全、图像与文本可搜索加密

娄芹：女，硕士生，研究方向为数字水印、人工智能安全

杨俊雪：女，博士生，研究方向为信息隐藏、人工智能安全

廖鑫：男，教授，研究方向为多媒体安全、数字取证、人工智能安全

通讯作者:
李谢华　beverly@hnu.edu.cn

中图分类号: TN911.73; TP309
计量
- 文章访问数: 458
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- PDF下载量: 59
- 被引次数: 0
出版历程
- 收稿日期: 2023-08-31
- 修回日期: 2024-03-21
- 网络出版日期: 2024-04-12
- 刊出日期: 2024-07-29

Screen-Shooting Resilient Watermarking Scheme Combining Invertible Neural Network and Inverse Gradient Attention

College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China

Funds: The National Natural Science Foundation of China (U22A2030, 61972142), Hunan Provincial Natural Science Foundation (2021JJ30140), Hunan Provincial Funds for Distinguished Young Scholars (2024JJ2025), Changsha Science and Technology Major Project (kh2205033)

摘要

摘要: 随着智能设备的普及，数字媒体内容的传播和分享变得更加便捷，人们可以通过手机拍摄屏幕等简单方式轻松获取未经授权的信息，导致屏幕拍摄传播成为版权侵权的热点问题。为此，该文针对屏幕盗摄版权保护任务提出一种端到端的基于可逆神经网络和逆梯度注意力的抗屏摄攻击图像水印框架，实现屏幕盗摄场景下版权维护的目标。该文将水印的嵌入和提取视为相互关联的逆问题，利用可逆神经网络实现编解码网络的一体化，有助于减少信息传递损失。进一步地，通过引入逆梯度注意模块，捕捉载体图像中鲁棒性强且视觉质量高的像素值，并将水印信息嵌入到载体图像中不易被察觉和破坏的区域，保证水印的不可见性和模型的鲁棒性。最后，通过可学习感知图像块相似度(LPIPS)损失函数优化模型参数，指导模型最小化水印图像感知差异。实验结果表明，所提方法在鲁棒性和水印图像视觉质量上优于目前同类的基于深度学习的抗屏摄攻击水印方法。
- 数字水印 /
- 可逆神经网络 /
- 逆梯度注意力 /
- 屏幕拍摄
Abstract: With the growing use of smart devices, the ease of sharing digital media content has been enhanced. Concerns have been raised about unauthorized access, particularly via screen shooting. In this paper, a novel end-to-end watermarking framework is proposed, employing invertible neural networks and inverse gradient attention, to tackle the copyright infringement challenges related to screen content leakage. A single invertible neural network is employed by the proposed method for watermark embedding and extraction, ensuring information integrity during network propagation. Additionally, robustness and visual quality are enhanced by an inverse gradient attention module, which emphasizes pixel values and embeds the watermark in imperceptible areas for better invisibility and model resilience. Model parameters are optimized using the Learnable Perceptual Image Patch Similarity (LPIPS) loss function, minimizing perception differences in watermarked images. The superiority of this approach over existing learning-based screen-shooting resilient watermarking methods in terms of robustness and visual quality is demonstrated by experimental results.
- Digital watermarking /
- Invertible neural network /
- Inverse gradient attention /
- Screen shooting

HTML全文

图 1 基于INN和逆梯度注意力的抗屏摄攻击水印框架

下载: 全尺寸图片幻灯片

图 2 逆梯度注意模块

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图 3 可逆嵌取模块

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图 4 不同方法的可视化水印图像

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图 5 不同强度失真条件下的水印提取准确率

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图 6 有/没有LPIPS损失引导的水印图像及水印残差

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表 1 视觉质量指标测试

方法	PSNR$ \uparrow $	SSIM$ \uparrow $	LPIPS$ \downarrow $
SSRW^[8]	37.776	0.969	0.033
HiDDeN^[11]	31.152	0.945	0.011
Stegastamp^[14]	29.419	0.906	0.063
PIMoG^[15]	36.411	0.975	0.009
本文	39.306	0.984	0.001

下载: 导出CSV

表 2 不同拍摄距离下的水印提取准确率(%)

距离(cm)	20	30	40	50	60
SSRW^[8]	92.38	90.00	71.19	95.00	82.97
HiDDeN^[11]	80.22	80.44	82.00	73.11	85.00
Stegastamp^[14]	99.93	89.99	90.93	85.33	97.10
PIMoG^[15]	97.78	99.78	99.33	94.52	99.17
本文	100	99.52	99.29	98.91	99.08

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表 3 不同水平拍摄角度下的水印提取准确率(%)

角度(°)	左45	左30	左15	右15	右30	右45
SSRW^[8]	83.21	88.45	79.04	92.38	81.91	77.86
HiDDeN^[11]	72.26	76.67	87.02	88.21	71.19	82.74
Stegastamp^[14]	97.13	99.03	97.20	97.48	86.47	96.37
PIMoG^[15]	96.91	94.64	98.21	99.88	96.79	98.93
本文	98.57	96.55	98.21	98.81	98.33	98.81

下载: 导出CSV

表 4 不同垂直拍摄角度下的水印提取准确率(%)

角度(°)	上45	上30	上15	下15	下30	下45
SSRW^[8]	76.67	95.12	98.33	82.62	93.69	84.05
HiDDeN^[11]	74.05	77.50	73.93	88.81	70.95	75.24
Stegastamp^[14]	95.40	98.03	99.17	98.41	98.13	88.67
PIMoG^[15]	99.52	99.76	95.78	99.05	96.43	96.19
本文	99.29	99.05	99.22	99.17	98.57	94.40

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表 5 不同显示/拍摄设备下的水印提取准确率(ENVISION/LG)

方法	HuaWei Nova7	Nikon Z30	HuaWei Mate30
SSRW^[8]	92.38/79.52	83.81/72.14	75.24/84.76
HiDDeN^[11]	80.22/66.22	81.78/77.11	73.56/69.33
Stegastamp^[14]	99.93/86.86	86.53/99.00	81.00/97.00
PIMoG^[15]	97.77/96.00	100/96.89	94.44/95.33
本文	100/97.11	99.76/98.10	90.95/99.29

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表 6 视觉质量指标测试($ {\mathrm{w}}\;\&\; {\mathrm{w}}/{\mathrm{o}}{\text{ IGA}} $)

视觉评价指标	PSNR$ \uparrow $	SSIM$ \uparrow $	LPIPS$ \downarrow $
$ {\mathrm{w}}/{\mathrm{o}}{\text{ IGA}} $	37.464	0.972	0.006
$ {\mathrm{w}}{\text{ IGA}} $	39.306	0.984	0.001

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表 7 不同拍摄距离的水印提取精度($ {\mathrm{w}}\;\&\; {\mathrm{w}}/{\mathrm{o}}{\text{ IGA}} $)

距离(cm)	20	30	40	50	60
$ {\mathrm{w}}/{\mathrm{o}}{\text{ IGA}} $	99.52	97.63	95.95	90.95	97.62
$ {\mathrm{w}}{\text{ IGA}} $	100	99.52	99.29	98.91	99.08

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