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High Capacity Reversible Data Hiding Algorithm in Encrypted Images Based on Image Adaptive MSB Prediction and Secret Sharing

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1139-1156页

作者： Kaili Qi Minqing Zhang Fuqiang Di Chao Jiang Key Laboratory of Network & Information Security under PAP Engineering University of PAPXi'an 710086China

Until now,some reversible data hiding in encrypted images(RDH-EI)schemes based on secret sharing(SIS-RDHEI)still have the problems of not realizing diffusivity and high embedding ***,this paper innovatively proposes a high capacity RDH-EI scheme that combines adaptive most significant bit(MSB)prediction with secret sharing ***,adaptive MSB prediction is performed on the original image and cryptographic feedback secret sharing strategy encrypts the spliced pixels to spare embedding *** the data hiding phase,each encrypted image is sent to a data hider to embed the secret information *** r copies of the image carrying the secret text are collected,the original image can be recovered lossless and the secret information can be *** evaluation shows that the proposed method in this paper has the diffusivity,reversibility,and *** last but the most important,it has higher embedding *** 512 ×512 grayscale images,the average embedding rate reaches 4.7358 bits per pixel(bpp).Compared to the average embedding rate that can be achieved by the Wang et al.'s SIS-RDHEI scheme,the proposed scheme with(2,2),(2,3),(2,4),(3,4),and(3,5)-threshold can increase by 0.7358 bpp,2.0658 bpp,2.7358 bpp,0.7358 bpp,and 1.5358 bpp,respectively.

关键词： adaptive most significant bit(MSB)prediction password feedback secret sharing high embedding capacity reversible data hiding in encrypted images

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MarkNeRF:Watermarking for Neural Radiance Field

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Computers, Materials & Continua 2024年第7期80卷 1235-1250页

作者： Lifeng Chen Jia Liu Wenquan Sun Weina Dong Xiaozhong Pan Cryptographic Engineering Department Institute of Cryptographic EngineeringEngineering University of PAPXi’an710086China Key Laboratory of Network and Information Security of PAP Xi’an710086China

This paper presents a novel watermarking scheme designed to address the copyright protection challenges encountered with Neural radiation field(NeRF)*** employ an embedding network to integrate the watermark into the images within the training ***,theNeRFmodel is utilized for *** copyright verification,a secret image is generated by inputting a confidential viewpoint into *** this basis,design an extraction network to extract embedded watermark images fromconfidential *** the event of suspicion regarding the unauthorized usage of NeRF in a black-box scenario,the verifier can extract the watermark from the confidential viewpoint to authenticate the model’s *** experimental results demonstrate not only the production of visually appealing watermarks but also robust resistance against various types of noise attacks,thereby substantiating the effectiveness of our approach in safeguarding NeRF.

关键词： Neural radiation field 3D watermark robustness black box

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RWNeRF:Robust Watermarking Scheme for Neural Radiance Fields Based on Invertible Neural networks

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Computers, Materials & Continua 2024年第9期80卷 4065-4083页

作者： Wenquan Sun Jia Liu Weina Dong Lifeng Chen Fuqiang Di Department of Cryptographic Engineering Engineering University of PAPXi’an710086China Department of Cryptographic Engineering Key Laboratory of PAP for Cryptology and Information SecurityXi’an710086China

As neural radiance fields continue to advance in 3D content representation,the copyright issues surrounding 3D models oriented towards implicit representation become increasingly *** response to this challenge,this paper treats the embedding and extraction of neural radiance field watermarks as inverse problems of image transformations and proposes a scheme for protecting neural radiance field copyrights using invertible neural network *** 2D image watermarking technology for 3D scene protection,the scheme embeds watermarks within the training images of neural radiance fields through the forward process in invertible neural networks and extracts them from images rendered by neural radiance fields through the reverse process,thereby ensuring copyright protection for both the neural radiance fields and associated 3D ***,challenges such as information loss during rendering processes and deliberate tampering necessitate the design of an image quality enhancement module to increase the scheme’s *** module restores distorted images through neural network processing before watermark ***,embedding watermarks in each training image enables watermark information extraction from multiple *** proposed watermarking method achieves a PSNR(Peak Signal-to-Noise Ratio)value exceeding 37 dB for images containing watermarks and 22 dB for recovered watermarked images,as evaluated on the Lego,Hotdog,and Chair datasets,*** results demonstrate the efficacy of our scheme in enhancing copyright protection.

关键词： Neural radiance fields 3D scene robust watermarking invertible neural networks

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High capacity reversible data hiding in encrypted images based on adaptive quadtree partitioning and MSB prediction

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Frontiers of information Technology & Electronic engineering 2023年第8期24卷 1156-1168页

作者： Kaili QI Minqing ZHANG Fuqiang DI Yongjun KONG Key Laboratory of Network&Information Security under Chinese People Armed Police Force Engineering University of PAPXi’an710086China

To improve the embedding capacity of reversible data hiding in encrypted images(RDH-EI),a new RDH-EI scheme is proposed based on adaptive quadtree partitioning and most significant bit(MSB)***,according to the smoothness of the image,the image is partitioned into blocks based on adaptive quadtree partitioning,and then blocks of different sizes are encrypted and scrambled at the block level to resist the analysis of the encrypted *** the data embedding stage,the adaptive MSB prediction method proposed by Wang and He(2022)is improved by taking the upper-left pixel in the block as the target pixel,to predict other pixels to free up more embedding *** the best of our knowledge,quadtree partitioning is first applied to *** results show that the proposed method is reversible and separable,and that its average embedding capacity is *** gray images with a size of 512×512,the average embedding capacity is increased by 25565 *** all smooth images with improved embedding capacity,the average embedding capacity is increased by about 35530 bits.

关键词： Adaptive quadtree partitioning Adaptive most significant bit(MSB)prediction Reversible data hiding in encrypted images(RDH-EI) High embedding capacity

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An Efficient and Secure Privacy-Preserving Federated Learning Framework Based on Multiplicative Double Privacy Masking

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Computers, Materials & Continua 2024年第9期80卷 4729-4748页

作者： Cong Shen Wei Zhang Tanping Zhou Yiming Zhang Lingling Zhang College of Cryptography Engineering Engineering University of PAPXi’an710086China Key Laboratory of People’s Armed Police for Cryptology and Information Security Xi’an710086China College of Information Engineering Engineering University of PAPXi’an710086China

With the increasing awareness of privacy protection and the improvement of relevant laws,federal learning has gradually become a new choice for cross-agency and cross-device machine *** order to solve the problems of privacy leakage,high computational overhead and high traffic in some federated learning schemes,this paper proposes amultiplicative double privacymask algorithm which is convenient for homomorphic addition *** combination of homomorphic encryption and secret sharing ensures that the server cannot compromise user privacy from the private gradient uploaded by the *** the same time,the proposed TQRR(Top-Q-Random-R)gradient selection algorithm is used to filter the gradient of encryption and upload efficiently,which reduces the computing overhead of 51.78%and the traffic of 64.87%on the premise of ensuring the accuracy of themodel,whichmakes the framework of privacy protection federated learning lighter to adapt to more miniaturized federated learning terminals.

关键词： Federated learning privacy protection homomorphic encryption double mask secret sharing gradient selection

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Reversible Data Hiding Algorithm in Encrypted Domain Based on Matrix Secret Sharing 5

Reversible Data Hiding Algorithm in Encrypted Domain Based o...

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5th International Conference on Electronics and Communication, network and Computer Technology, ECNCT 2023

作者： Jiang, Zongbao Zhang, Minqing Ke, Yan Jiang, Chao University of PAP Key Laboratory of Network and Information Security of PAP Engineering Xi'an710086 China

ISBN: (纸本)9798350328660

Focused on the problems of user privacy disclosure and ciphertext data management in cloud service environment, this paper proposes a reversible data hiding algorithm in encrypted domain based on matrix secret sharing. Firstly, the matrix secret sharing of the pixels of the encrypted image was used to obtain n shares, in the process of constructing shared vectors for secret segmentation, the random variables involved in encryption and the location index of ciphertext feedback were used to construct the mapping relationship between them and the secret data to be embedded to generate redundant space for data embedding. Finally, the overflow pixels were processed, and the edge information was embedded by differential extension technology to obtain n marked encrypted images. The experimental results showed that the algorithm has complete reversibility. In contrast to the current algorithms, the embedding rate of the algorithm was greatly improved. The maximized embedding rate of the (3,4)-threshold scheme was 4.00bpp, and the maximized embedding rate of the (5,5)-threshold scheme was 6.40bpp, and the statistical characteristics such as correlation coefficient of the marked encrypted images were consistent with those of random image, and the security was high. The algorithm not only has the functions of privacy data protection and ciphertext authentication management, but also has the disaster recovery feature to realize the multi-party security storage of data. © 2023 IEEE.

关键词： Matrix algebra

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MarkINeRV: A Robust Watermarking Scheme for Neural Representation for Videos Based on Invertible Neural networks

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Computers, Materials & Continua 2024年第9期80卷 4031-4046页

作者： Wenquan Sun Jia Liu Lifeng Chen Weina Dong Fuqiang Di Department of Cryptographic Engineering Engineering University of PAPXi’an710086China Department of Cryptographic Engineering Key Laboratory for Cryptology and Information SecurityXi’an710086China

Recent research advances in implicit neural representation have shown that a wide range of video data distributions are achieved by sharing model weights for Neural Representation for Videos(NeRV).While explicit methods exist for accurately embedding ownership or copyright information in video data,the nascent NeRV framework has yet to address this issue *** response,this paper introduces MarkINeRV,a scheme designed to embed watermarking information into video frames using an invertible neural network watermarking approach to protect the copyright of NeRV,which models the embedding and extraction of watermarks as a pair of inverse processes of a reversible network and employs the same network to achieve embedding and extraction of *** is just that the information flow is in the opposite ***,a video frame quality enhancement module is incorporated to mitigate watermarking information losses in the rendering process and the possibility ofmalicious attacks during transmission,ensuring the accurate extraction of watermarking information through the invertible network’s inverse *** paper evaluates the accuracy,robustness,and invisibility of MarkINeRV through multiple video *** results demonstrate its efficacy in extracting watermarking information for copyright protection of *** represents a pioneering investigation into copyright issues surrounding NeRV.

关键词： Invertible neural network neural representations for videos watermarking robustness

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Reversible Data Hiding in Encrypted Images based on Classic McEliece Cryptosystem 2

Reversible Data Hiding in Encrypted Images based on Classic ...

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2nd International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Jiang, Zongbao Zhang, Minqing Di, Fuqiang Dong, Weina University of PAP Key Laboratory of Network and Information Security of PAP Engineering Shaanxi Xi'an710086 China

ISBN: (纸本)9798350331417

Aiming at the problems that the security of traditional data hiding algorithms is difficult to be guaranteed and the existing algorithms' embedded volume is restricted under quantum operation attacks, a reversible data hiding algorithm in encrypted images based on Classic McEliece cryptosystem is proposed. The image pixels are first decomposed into bit planes to generate a data stream, and then encrypted in groups using the Classic McEliece cipher algorithm. In the encryption process, the fault tolerance mechanism in its encryption algorithm is utilized to select the appropriate modulation noise composition and the mapping relationship between the secret data, and the error pattern is modulated into noise to interfere with the encryption process, so as to realize the embedding of the secret data. Finally, the marked encrypted image is obtained by using the exclusive-or encryption and scrambling encryption processes. The experimental results show that the algorithm has the reversibility of carrier recovery. Compared with the existing algorithms, this algorithm's embedded volume is advantageous, and its highest embedding rate is 4.43548 bpp, which realizes large-capacity data hiding while improving security. © 2023 IEEE.

关键词： Classic McEliece error-correcting codes high-capacity hiding reversible data hiding in encrypted images

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A New High-Capacity Robust Image Steganography Method via Adversarial network 5

A New High-Capacity Robust Image Steganography Method via Ad...

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5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023

作者： Dong, Weina Jiang, Zongbao Chen, Lifeng Sun, Wenquan University of PAP Key Laboratory of Network and Information Security of PAP Engineering Shaanxi Xi'an710086 China

ISBN: (纸本)9798350357738

Aiming at the problem that the high-capacity steganography model based on encoding-decoding network had weak robustness and could not resist noise attack and channel compression, an improved high-capacity robust image steganography algorithm based on adversarial network was proposed. In the proposed algorithm, encoder, decoder and critic based on densely connected convolutional networks (DenseNet) were designed. The secret information and the carrier image are jointly encoded into a steganographic image by the encoder, then the secret information was extracted by the decoder. The critic was used to discriminate between carrier images and steganographic images. A noise layer was added between encoder and decoder, Dropout, JPEG compression, gaussian blur, gaussian noise and salt and pepper noise were used to simulate a real environment of various kinds of noise attack. The steganography output by encoder was processed with different kinds of noise and decoded by decoder. Through model was trained, the message could be extracted from the noise-processed steganographic image by the decoder, so the ability to resist noise attacks was realized. The experiment results show that the steganographic capacity of the proposed algorithm reaches 0.450.95 bits per pixel (bpp) on 360 × 360 pixel images, which are 3.6 times higher than that of the earlier robust steganographic algorithms. The decoding accuracy reaches 0.72-0.97, which are 42% points higher than that of steganography without noise layer. The proposed algorithm not only guarantees high embedding quantity and high coding image quality, but also has stronger anti-noise capability. © 2023 IEEE.

关键词： Steganography

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SFSDA:Secure and Flexible Subset Data Aggregation with Fault Tolerance for Smart Grid

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Intelligent Automation & Soft Computing 2023年第8期37卷 2477-2497页

作者： Dong Chen Tanping Zhou Xu An Wang Zichao Song Yujie Ding Xiaoyuan Yang College of Cryptography Engineering Engineering University of PAPXi’an710086China Key Laboratory of Network and Information Security under the PAP Xi’an710086China Institute of Software Chinese Academy of Sciences Beijing100080China

Smart grid(SG)brings convenience to users while facing great chal-lenges in protecting personal private *** aggregation plays a key role in protecting personal privacy by aggregating all personal data into a single value,preventing the leakage of personal data while ensuring its ***,a flexible subset data aggregation(FSDA)scheme based on the Pail-lier homomorphic encryption was first proposed by Zhang et *** scheme can dynamically adjust the size of each subset and obtain the aggregated data in the corresponding *** this paper,firstly,an efficient attack with both theorems proving and experimentative verification is *** find that in a specific scenario where the encrypted data constructed by a smart meter(SM)exceeds the size of one Paillier ciphertext,the malicious fog node(FN)may use the received ciphertext to obtain the reading of the ***,to avoid the possibility of privacy disclosure under certain circumstances,additional hash functions are added to the individual encryption *** addition,fault tolerance is very important to aggregation schemes in practical *** most of the current schemes,once some SMs failed,then they will not *** far as we know,there is no multi-subset aggregation scheme both supports flexible subset data aggregation and fault ***,we construct the first secure flexible subset data aggregation(SFSDA)scheme with fault tolerance by combining the fault tolerance method with the flexible multi-subset aggregation,where FN enables the control server(CS)to finally decrypt the aggregated ciphertext by recovering equivalent ciphertexts when some SMs fail to submit their *** show that our SFSDA scheme keeps the efficiency in implementing a flexible multi-subset aggregation function,and only has a small delay in implementing fault-tolerant data aggregation.

关键词： Flexible subset aggregation fault tolerance privacy preservation smart grid

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