载体独立的抗屏摄信息膜叠加水印算法

doi:10.11959/j.issn.2096-109x.2023045

Abstract

Abstract:

Financial security, an important part of national security, is critical for the stable and healthy development of the economy.Digital image watermarking technology plays a crucial role in the field of financial information security, and the anti-screen watermarking algorithm has become a new research focus of digital image watermarking technology.The common way to achieve an invisible watermark in existing watermarking schemes is to modify the carrier image, which is not suitable for all types of images.To solve this problem, an end-to-end robust watermarking scheme based on deep learning was proposed.The algorithm achieved both visual quality and robustness of the watermark image.A random binary string served as the input of the encoder network in the proposed end-to-end network architecture.The encoder can generate the watermark information overlay, which can be attached to any carrier image after training.The ability to resist screen shooting noise was learned by the model through mathematical methods incorporated in the network to simulate the distortion generated during screen shooting.The visual quality of the watermark image was further improved by adding the image JND loss based on just perceptible difference.Moreover, an embedding hyperparameter was introduced in the training phase to balance the visual quality and robustness of the watermarked image adaptively.A watermark model suitable for different scenarios can be obtained by changing the size of the embedding hyperparameter.The visual quality and robustness performance of the proposed scheme and the current state-of-the-art algorithms were evaluated to verify the effectiveness of the proposed scheme.The results show that the watermark image generated by the proposed scheme has better visual quality and can accurately restore the embedded watermark information in robustness experiments under different distances, angles, lighting conditions, display devices, and shooting devices.

Key words: deep learning, robust watermarking, screen-shooting, visual quality, just noticeable difference

CLC Number:

TP393

Xiaomeng LI, Daidou GUO, Xunfang ZHUO, Heng YAO, Chuan QIN. Carrier-independent screen-shooting resistant watermarking based on information overlay superimposition[J]. Chinese Journal of Network and Information Security, 2023, 9(3): 135-149.

Figures/Tables 19

攻击类型	攻击强度	本文算法
攻击类型	攻击强度	$ε_{1}$	$ε_{2}$
	4/5	97.15%	96.12%
	3/4	97.15%	95.15%
缩放攻击	3/5	97.18%	97.09%
（缩放比例）	1/2	97.00%	94.17%
	2/5	96.70%	94.17%
	1/4	96.42%	93.20%
	1/5	95.55%	89.47%
	13	97.03%	96.12%
高斯模糊	15	96.54%	96.12%
（高斯核大小）	17	95.18%	92.23%
	19	93.05%	80.58%
	21	90.96%	67.96%
	40	96.30%	90.29%
	50	96.63%	94.17%
JPEG压缩	60	96.75%	96.12%
（质量因子大小）	70	96.69%	96.12%
	80	96.91%	95.15%
	90	96.88%	97.09%
	20 × 20	97.09%	97.09%
	40 × 40	96.94%	94.17%
裁剪攻击	60 × 60	97.21%	98.06%
（裁剪区域尺寸）	80 × 80	96.91%	97.09%
	100 × 100	96.18%	97.09%
	120 × 120	94.45%	93.20%
	10 pt	95.93%	90.29%
边缘遮盖	20 pt	95.27%	87.38%
（遮盖宽度）	30 pt	95.33%	90.29%
	40 pt	94.66%	89.32%
	50 pt	94.57%	82.52%

拍摄距离/cm	$ε_{2}$
拍摄距离/cm	StegaStamp^[18]	提出的算法
20	83.33%	100.00%
35	100.00%	100.00%
55	100.00%	100.00%

拍摄角度	$ε_{1}$	$ε_{1}$	$ε_{1}$	$ε_{1}$	$ε_{2}$	$ε_{1}$	$ε_{2}$
拍摄角度	Kang等^[30]	Pramila等^[4]	Nakamura等^[31]	StegaStamp^[18]		本文算法
+45°	58.89%	55.28%	83.69%	99.22%	100.00%	97.19%	100.00%
+30°	72.17%	57.00%	86.81%	99.22%	100.00%	97.19%	100.00%
+15°	80.47%	54.88%	77.73%	99.48%	100.00%	97.19%	100.00%
0°	67.50%	55.95%	84.56%	99.74%	100.00%	96.56%	100.00%
-15°	74.31%	64.03%	86.13%	99.74%	100.00%	98.13%	100.00%
-30°	73.52%	63.48%	83.69%	99.74%	100.00%	98.13%	100.00%
-45°	80.08%	71.39%	84.17%	98.69%	100.00%	97.19%	100.00%

光照强度/lux	$ε_{1}$	$ε_{2}$	$ε_{1}$	$ε_{2}$
光照强度/lux	StegaStamp^[18]		本文算法
50	100.00%	100.00%	98.75%	100.00%
150	99.74%	100.00%	96.56%	100.00%
500	99.74%	100.00%	98.75%	100.00%

设备	名称	$ε_{1}$	$ε_{2}$
	iPhone XS	96.56%	100%
拍摄设备	Xiaomi 11	97.44%	100%
	HUAWEI P40 Pro+	98.18%	100%
	MacBook Pro 2019	96.56%	100%
显示设备	Dell G15 5511	98.18%	100%
	AOC I2490PXZ	98.32%	100%

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算法	PSNR/dB	SSIM	LPIPS
StegaStamp^[18]	27.24	0.902 6	0.0511
本文算法(α=1.5)	28.64	0.967 2	0.009 9
本文算法(α=1.6)	30.56	0.968 0	0.009 3
本文算法(α=1.7)	31.76	0.974 0	0.007 5

Carrier-independent screen-shooting resistant watermarking based on information overlay superimposition

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