Active protection method for deep neural network model based on four-dimensional Chen chaotic system

doi:10.11772/j.issn.1001-9081.2024111583

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (11): 3621-3631.DOI: 10.11772/j.issn.1001-9081.2024111583

• Cyber security • Previous Articles

Active protection method for deep neural network model based on four-dimensional Chen chaotic system

Xintao DUAN¹^,²(), Mengru BAO¹, Yinhang WU¹, Chuan QIN³

^1.School of Computer and Information Engineering，Henan Normal University，Xinxiang Henan 453007，China
^2.Henan Key Laboratory of Educational Artificial Intelligence and Personalized Learning （Henan Normal University），Xinxiang Henan 453007，China
^3.School of Optical-Electrical and Computer Engineering，University of Shanghai for Science and Technology，Shanghai 200093，China

Received:2024-11-07 Revised:2025-02-16 Accepted:2025-02-19 Online:2025-02-21 Published:2025-11-10
Contact: Xintao DUAN
About author:BAO Mengru， born in 1999， M. S. candidate. Her research interests include deep learning， model protection.
WU Yinhang， born in 1997， M. S. candidate. His research interests include deep learning， model protection.
QIN Chuan， born in 1980， Ph. D.， professor. His research interests include digital image processing， information hiding， deep learning.
Supported by:
Key Scientific Research Project of Universities in Henan Province(23A520006);Science and Technology Research Project of Henan Province(222102210199)

基于四维Chen混沌系统的深度神经网络模型主动保护方法

段新涛¹^,²(), 保梦茹¹, 武银行¹, 秦川³

^1.河南师范大学计算机与信息工程学院，河南新乡 453007
^2.教育人工智能与个性化学习河南省重点实验室（河南师范大学），河南新乡 453007
^3.上海理工大学光电信息与计算机工程学院，上海 200093

通讯作者: 段新涛
作者简介:保梦茹（1999—），女，河南郑州人，硕士研究生，主要研究方向：深度学习、模型保护
武银行（1997—），男，河南周口人，硕士研究生，主要研究方向：深度学习、模型保护
秦川（1980—），男，安徽芜湖人，教授，博士，主要研究方向：数字图像处理、信息隐藏、深度学习。
基金资助:
河南省高等学校重点科研项目(23A520006);河南省科技攻关项目(222102210199)

Abstract

Abstract:

Deep Neural Network （DNN）-based models have been widely applied due to their superior performance. However， training a powerful DNN model requires extensive datasets， expertise， computational resources， specialized hardware， and significant time investment. Unauthorized exploitation of such models could cause substantial losses to model owners. Aiming at the security and intellectual property issues of DNN models， an active protection method was proposed. The method employed a new comprehensive weight selection strategy to precisely identify critical weights within the model. Combining with the structural characteristics of the convolutional layer in DNN model， the four-dimensional Chen chaotic system was introduced for the first time on the basis of the three-dimensional chaotic system to scramble and encrypt a small number of weights in the convolutional layer. Meanwhile， to address the problem that authorized users cannot decrypt even with the key， an Elliptic Curve Cryptography （ECC）-based digital signature scheme was integrated for encryption models. After encryption， the weight positions and the initial values of chaotic sequence were combined to form an encryption key. Authorized users can use the key to correctly decrypt the DNN model， while unauthorized attackers cannot functionally use intercepted models even if acquired. Experimental results show that scrambling a minimal fraction of weight positions significantly degrades classification accuracy， and the decryption model can be restored without any loss. In addition， the method is resistant to fine-tuning and pruning attacks， and the obtained key has strong sensitivity and is resistant to brute force attacks. Furthermore， the experiments verify the method’s transferability， it is effective for image classification models， and can protect deep image steganography models and object detection models simultaneously.

Key words: AI model security, Deep Neural Network (DNN), weight encryption, four-dimensional Chen chaotic system, Elliptic Curve Cryptography (ECC) algorithm

摘要：

基于深度神经网络（DNN）的模型以其优越的性能得到了广泛的应用，但训练一个性能强大的DNN模型需要大量的数据集、专业知识、计算资源、硬件条件和时间等，如果对它进行非法盗用会对模型拥有者造成巨大的损失。针对DNN模型的安全和知识产权问题，提出一种DNN模型主动保护方法。该方法使用一种新的综合性权重选择策略精准定位模型中的重要权重，并结合DNN模型卷积层的结构特点，在三维混沌系统的基础上首次引入四维Chen混沌系统对卷积层的少量权重进行位置置乱加密。同时，为了解决授权用户即使拥有密钥也无法解密的问题，结合椭圆曲线加密算法（ECC）构建加密模型的数字签名方案。加密后，权重位置和混沌序列的初始值复合形成加密密钥，授权用户可以使用该密钥正确解密DNN模型，而未被授权的攻击者即使截获了DNN模型也无法正常使用。实验结果表明，对分类模型的少量权重位置进行置乱能显著降低分类准确率，并且解密模型可以实现无损恢复。此外，该方法能够抵抗微调和剪枝攻击，且得到的密钥具有较强的敏感性并能抵抗暴力攻击。同时，通过实验验证了该方法不仅对图像分类模型有效，还能保护深度图像隐写模型和目标检测模型，具有可迁移性。

关键词: AI模型安全, 深度神经网络, 权重加密, 四维Chen混沌系统, 椭圆曲线加密算法

CLC Number:

TP309.2

Xintao DUAN, Mengru BAO, Yinhang WU, Chuan QIN. Active protection method for deep neural network model based on four-dimensional Chen chaotic system[J]. Journal of Computer Applications, 2025, 45(11): 3621-3631.

段新涛, 保梦茹, 武银行, 秦川. 基于四维Chen混沌系统的深度神经网络模型主动保护方法[J]. 《计算机应用》唯一官方网站, 2025, 45(11): 3621-3631.

Figures/Tables 17

Fig. 1 Main workflow of proposed method

Fig. 2 Encryption process

Fig. 3 Phase diagrams of y-z and x-y-w hyperchaotic attractors

Tab. 1 Test results on CIFAR-10 and Fashion-MNIST datasets

数据集	分类模型	原始准确率	加密准确率	恢复准确率
CIFAR-10	ResNet18	95.09	10.13	95.09
	ResNet50	96.44	10.00	96.44
	MobileNetV2	95.12	12.41	95.12
	MobileNetV3	95.09	9.82	95.09
	ShuffleNet	96.26	8.77	96.26
	EfficientNet	94.34	9.99	94.34
	VGG16	91.04	10.63	91.04
Fashion- MNIST	ResNet18	92.33	12.73	92.33
	ResNet50	91.56	10.00	91.56
	MobileNetV2	92.66	10.00	92.66
	MobileNetV3	92.91	9.90	92.91
	ShuffleNet	90.48	11.09	90.48
	EfficientNet	94.57	10.00	94.57
	VGG16	90.92	9.89	90.92

Tab. 2 Test results on CIFAR-100 and ImageNet datasets

数据集	分类模型	原始准确率	加密准确率	恢复准确率
CIFAR-100	ResNet18	80.59	0.84	80.59
	ResNet50	82.75	1.00	82.75
	MobileNetV2	81.37	1.06	81.37
	MobileNetV3	81.57	1.86	81.57
	ShuffleNet	82.89	1.18	82.89
	EfficientNet	84.19	0.86	84.19
	VGG16	80.26	1.26	80.26
ImageNet	ResNet18	84.65	0.44	84.65
	ResNet50	89.67	0.50	89.67
	MobileNetV2	86.42	0.47	86.42
	MobileNetV3	89.45	0.37	89.45
	ShuffleNet	87.24	0.47	87.24
	EfficientNet	94.61	0.50	94.61
	VGG16	82.68	0.50	82.68

Fig. 4 Test accuracy of DNN model with different encryption proportions

Tab. 3 Verification results of ECC digital signature

数据集	分类模型	篡改	验证结果	解密准确率/%
ImageNet	ShuffleNet	否	True	87.24
ImageNet	ShuffleNet	是	False	0.56
CIFAR-100	MobileNetV2	否	True	81.37
CIFAR-100	MobileNetV2	是	False	1.32

Tab. 4 Classification accuracy comparison of encryption models under different pruning rates

剪枝率	分类准确率/%
剪枝率	EfficientNet （CIFAR-10）	VGG16 （CIFAR-100）	ShuffleNet （Fashion-MNIST）	ResNet18 （ImageNet）
0.1	9.98	1.23	19.40	0.58
0.2	10.00	1.23	17.73	0.51
0.3	10.00	1.33	10.65	0.53
0.4	9.90	1.07	12.63	0.62
0.5	10.00	1.04	8.58	0.57
0.6	10.00	1.07	10.00	0.53
0.7	11.12	0.74	10.00	0.51
0.8	10.00	1.01	10.00	0.50
0.9	10.00	1.09	10.00	0.50

Tab. 5 Classification accuracy comparison of encryption models after fine-tuning attack

轮数	分类准确率/%
轮数	ShuffleNet （CIFAR-10）	ResNet18 （CIFAR-100）	MobileNetV2 （Fashion-MNIST）	MobileNetV3 （ImageNet）
10	1.44	5.82	9.45	0.61
20	4.74	14.34	14.79	1.07
30	15.63	14.54	14.97	2.42
40	17.13	14.75	15.01	3.50
50	23.86	14.83	14.51	4.82
60	28.16	14.84	15.07	5.74
70	29.41	14.92	14.56	6.56
80	33.20	14.93	15.31	7.38
90	35.61	14.93	15.54	7.94
100	46.60	15.08	15.76	8.55

Tab. 6 Objective effects of key sensitivity

数据集	分类模型	分类准确率/%
数据集	分类模型	原始密钥 $K 1$	扰动密钥 $K 1'$
CIFAR-10	MobileNetV3	95.09	9.56
CIFAR-100	ShuffleNet	82.89	1.07
Fashion-MNIST	VGG16	90.92	10.67
ImageNet	MobileNetV2	86.42	0.50

Tab. 6 Objective effects of key sensitivity

数据集	分类模型	分类准确率/%
数据集	分类模型	原始密钥 $K 1$	扰动密钥 $K 1'$
CIFAR-10	MobileNetV3	95.09	9.56
CIFAR-100	ShuffleNet	82.89	1.07
Fashion-MNIST	VGG16	90.92	10.67
ImageNet	MobileNetV2	86.42	0.50

Tab. 7 Comparison of encryption and decryption time， and model inference time

分类模型	加密时间	解密时间	处理单张图片的推理时间
分类模型	加密时间	解密时间	原始模型	解密模型
ResNet18	0.430	0.340	0.049	0.045
ResNet50	1.356	1.121	0.157	0.166
VGG16	2.160	2.254	0.198	0.206

Tab. 8 Storage and transmission costs before and after encryption

分类模型	存储大小/MB		传输大小/MB		传输开销增加比例/%
分类模型	加密前	加密后	加密前	加密后	传输开销增加比例/%
ResNet50	90.745	90.731	90.745	91.083	0.373
MobileNetV2	9.205	9.196	9.205	9.237	0.343
EfficientNet	203.217	203.142	203.271	203.823	0.298
VGG16	515.301	515.297	515.301	517.465	0.420

Tab. 9 Objective indicators of secret images extracted by Baluja and HiDDeN extraction networks after encryption

DNN模型	类别	PSNR/dB	SSIM	LPIPS
Baluja	原始模型	34.627 5	0.967 8	0.031 0
	解密模型	34.627 5	0.967 8	0.031 0
	加密模型	10.991 9	0.326 0	0.801 1
HiDDeN	原始模型	31.462 5	0.960 2	0.082 3
	解密模型	31.462 5	0.960 2	0.082 3
	加密模型	11.232 6	0.270 1	0.725 0

Fig. 5 Subjective effects of secret images extracted by Baluja and HiDDeN extraction networks after encryption

Tab. 10 Objective test results of Faster R-CNN and Mask R-CNN models

DNN模型	mIoU
DNN模型	原始模型	加密模型	解密模型
Faster R-CNN	81.73	3.41	81.73
Mask R-CNN	82.30	18.72	82.30

Fig. 6 Subjective test results of Faster R-CNN model

Tab. 11 Comparison of proposed method and SOTAs

方法	数据集	原始准确率	加密准确率	重训练	硬件支持	权重选择
文献［5］方法	CIFAR-10	86.96	15.00	√	×	×
文献［5］方法	Fashion-MNIST	—	—	√	×	×
文献［11］方法	CIFAR-10	89.54	9.37	√	√	×
文献［11］方法	Fashion-MNIST	89.93	10.05	√	√	×
文献［14］方法	CIFAR-10	92.02	10.86	√	×	√
文献［14］方法	Fashion-MNIST	91.01	10.36	√	×	√
文献［15］方法	CIFAR-10	91.24	10.00	√	×	√
文献［15］方法	Fashion-MNIST	—	—	√	×	√
文献［17］方法	CIFAR-10	93.91	10.48	√	×	√
文献［17］方法	Fashion-MNIST	93.97	10.00	√	×	√
本文方法	CIFAR-10	96.26	8.77	×	×	√
本文方法	Fashion-MNIST	94.57	10.00	×	×	√

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Active protection method for deep neural network model based on four-dimensional Chen chaotic system

基于四维Chen混沌系统的深度神经网络模型主动保护方法

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