Efficient Hashing Method Using 2D-2D PCA for Image Copy Detection

Liang, Xiaoping; Tang, Zhenjun; Huang, Ziqing; Zhang, Xianquan; Zhang, Shichao

doi:10.1109/tkde.2021.3131188

Cited by 34 publications

(14 citation statements)

References 64 publications

(92 reference statements)

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“…Therefore, they are extremely sensitive to data bit changes, i.e., changing only 1 bit of the data will significantly change the output. Different from cryptographic hashing, perceptual hashing [1,12,21,28,35,44,46,47] can verify the integrity of the data when the data is modified in a content-preserving way. With perceptual hashing, the hash codes of the original image and the modified image are still similar if images are modified by image operators such as image filtering, JPEG compression, or image resize.…”

Section: Image Hashingmentioning

confidence: 99%

Perceptual Hashing of Deep Convolutional Neural Networks for Model Copy Detection

Chen¹,

Zhou²,

Zhang³

et al. 2023

Preprint

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This paper has been accepted by ACM TOMM. https://dl.acm.org/doi/pdf/10.1145/3572777 In recent years, many model intellectual property (IP) proof methods for IP protection have been proposed, such as model watermarking and model fingerprinting. However, as an important part of the model IP protection system, the model copy detection task has not received enough attention. With the increasing number of neural network models transmitted and deployed on the Internet, the search for similar models is in great demand, which concurrently triggers the security problem of copy detection of models for IP protection. Due to the high computational complexity, both model watermarking and model fingerprinting lack the capability to efficiently find suspected infringing models among tens of millions of models. In this paper, inspired by the hash-based image retrieval methods, we propose a perceptual hashing algorithm for convolutional neural networks (CNNs). The proposed perceptual hashing algorithm can convert the weights of CNNs to fixed-length binary hash codes so that the lightly modified version has the similar hash code as the original model. By comparing the similarity of a pair of hash codes between a query model and a test model in the model library, the similar versions of a query model can be retrieved efficiently. To the best of our knowledge, this is the first perceptual hashing algorithm for CNNs. The experiment performed on a model library containing 3,565 models indicates that our proposed perceptual hashing scheme has a superior copy detection performance.

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Section: Image Hashingmentioning

confidence: 99%

Perceptual Hashing of Deep Convolutional Neural Networks for Model Copy Detection

Chen¹,

Zhou²,

Zhang³

et al. 2023

Preprint

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show abstract

Section: Image Hashingmentioning

confidence: 99%

Perceptual Hashing of Deep Convolutional Neural Networks for Model Copy Detection

Chen

Zhou

Zhang

et al. 2023

ACM Trans. Multimedia Comput. Commun. Appl.

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In recent years, many model intellectual property (IP) proof methods for IP protection have been proposed, such as model watermarking and model fingerprinting. However, with the increasing number of models transmitted and deployed on the Internet, quickly finding the suspect model among thousands of models on the model-sharing platforms such as GitHub is in great demand, which concurrently triggers the new security problem of model copy detection for IP protection. And as an important part of the model IP protection system, the model copy detection task has not received enough attention. Due to the high computational complexity, both model watermarking and model fingerprinting lack the capability to efficiently find suspected infringing models among tens of millions of models. In this paper, inspired by the hash-based image retrieval methods, we introduce a novel model copy detection mechanism: perceptual hashing for convolutional neural networks (CNNs). The proposed perceptual hashing algorithm can convert the weights of CNNs to fixed-length binary hash codes so that the lightly modified version has the similar hash code as the original model. By comparing the similarity of a pair of hash codes between a query model and a test model in the model library, similar versions of a query model can be retrieved efficiently. To the best of our knowledge, this is the first perceptual hashing algorithm for deep neural network models. Specifically, we first select the important model weights based on the model compression theory, then calculate the normal test statistics (NTS) on the segments of important weights, and finally encode the NTS features into hash codes. The experiment performed on a model library containing 3,565 models indicates that our perceptual hashing scheme has a superior copy detection performance.

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“…[3][4][5] In recent years, some researchers have proposed using hashing schemes 6,7 to detect copy versions of multimedia data for copyright protection. [8][9][10] Video hashing is an effective technique for video data processing. 11,12 In practical applications, the use of a hash for representing the video itself can reduce storage cost and speed up similarity computation.…”

Section: Introductionmentioning

confidence: 99%

Robust video hashing based on deep feature and quaternion generic Fourier descriptor for copy detection

Yu,

Tang,

Liang

et al. 2024

J. Electron. Imag.

Self Cite

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Robust video hashing is an effective method of copy detection. But most existing schemes do not make effective classifications; thus their copy detection performances are unsatisfactory. To address these problems, we propose a robust video hashing based on deep feature and the quaternion generic Fourier descriptor (QGFD) for copy detection. In the proposed hashing scheme, an entropy-weighted secondary frame is calculated by weighting all frames in a video group via color entropy. Because color entropy can reflect frame color information, the incorporation of color entropy into each frame guarantees the discrimination of the proposed hashing scheme and makes the obtained entropy-weighted secondary frame discriminative. Next, the entropy-weighted secondary frames are used to extract deep features and geometric invariant features. The deep features are extracted from the low-frequency discrete cosine transform (DCT) coefficients of feature maps learned by three Inception modules of GoogLeNet. As the low-frequency DCT coefficients indicate feature map content and are stable, the deep features provide good discrimination and robustness. The geometric invariant features are extracted by QGFD. Because QGFD are geometric invariant, the use of QGFD can improve the robustness of the proposed hashing scheme. Hash is ultimately determined by quantifying and combining deep features and geometric invariant features. Many experiments were performed on open datasets and indicate that the proposed hashing scheme is superior to the evaluated hashing schemes in the task of copy detection and classification.

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Efficient Hashing Method Using 2D-2D PCA for Image Copy Detection

Cited by 34 publications

References 64 publications

Perceptual Hashing of Deep Convolutional Neural Networks for Model Copy Detection

Perceptual Hashing of Deep Convolutional Neural Networks for Model Copy Detection

Perceptual Hashing of Deep Convolutional Neural Networks for Model Copy Detection

Robust video hashing based on deep feature and quaternion generic Fourier descriptor for copy detection

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