Video Hashing Research Articles

Perceptual Video Hashing With Secure Anti-Noise Model for Social Video Retrieval

Perceptual Video Hashing With Secure Anti-Noise Model for Social Video Retrieval

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

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

Efficient Unsupervised Video Hashing With Contextual Modeling and Structural Controlling

Efficient Unsupervised Video Hashing With Contextual Modeling and Structural Controlling

Video Hashing with Tensor Robust PCA and Histogram of Optical Flow for Copy Detection

Abstract This paper proposes a novel video hashing with tensor robust Principal Component Analysis (PCA) and Histogram of Optical Flow (HOF) for copy detection. In the proposed hashing, a video is divided into some video groups. For each video group, a low-rank secondary frame is constructed from the low-rank component decomposed by applying tensor robust PCA to the video group. Since the low-rank component can well indicate spatial-temporal intrinsic structure of the video group and it is slightly disturbed by digital operations, feature extraction from the low-rank secondary frames is discriminative and stable. Next, spatial features and temporal features are extracted from low-rank secondary frames by Charlier moments and HOF, respectively. Since the Charlier moments are robust to geometric transform and they can efficiently distinguish video frames with different contents, the use of Charlier moments can make robust and discriminative spatial features. As the HOF can measure the distribution of motion information between frames, the temporal features formed by HOFs can provide good discrimination. Hash is ultimately determined by quantizing the spatial and temporal features and concatenating the quantized results. Numerous experiments on open video datasets indicate that the proposed hashing is superior to some hashing baseline schemes in terms of classification and copy detection.

An efficient and robust supervised video hashing scheme based on a timedistributed CNN-BLSTM model and principal component analysis

An efficient and robust supervised video hashing scheme based on a timedistributed CNN-BLSTM model and principal component analysis

Live laparoscopic video retrieval with compressed uncertainty.

Searching through large volumes of medical data to retrieve relevant information is a challenging yet crucial task for clinical care. However the primitive and most common approach to retrieval, involving text in the form of keywords, is severely limited when dealing with complex media formats. Content-based retrieval offers a way to overcome this limitation, by using rich media as the query itself. Surgical video-to-video retrieval in particular is a new and largely unexplored research problem with high clinical value, especially in the real-time case: using real-time video hashing, search can be achieved directly inside of the operating room. Indeed, the process of hashing converts large data entries into compact binary arrays or hashes, enabling large-scale search operations at a very fast rate. However, due to fluctuations over the course of a video, not all bits in a given hash are equally reliable. In this work, we propose a method capable of mitigating this uncertainty while maintaining a light computational footprint. We present superior retrieval results (3%-4% top 10 mean average precision) on a multi-task evaluation protocol for surgery, using cholecystectomy phases, bypass phases, and coming from an entirely new dataset introduced here, surgical events across six different surgery types. Success on this multi-task benchmark shows the generalizability of our approach for surgical video retrieval.

Contrastive Masked Autoencoders for Self-Supervised Video Hashing

Self-Supervised Video Hashing (SSVH) models learn to generate short binary representations for videos without ground-truth supervision, facilitating large-scale video retrieval efficiency and attracting increasing research attention. The success of SSVH lies in the understanding of video content and the ability to capture the semantic relation among unlabeled videos. Typically, state-of-the-art SSVH methods consider these two points in a two-stage training pipeline, where they firstly train an auxiliary network by instance-wise mask-and-predict tasks and secondly train a hashing model to preserve the pseudo-neighborhood structure transferred from the auxiliary network. This consecutive training strategy is inflexible and also unnecessary. In this paper, we propose a simple yet effective one-stage SSVH method called ConMH, which incorporates video semantic information and video similarity relationship understanding in a single stage. To capture video semantic information for better hashing learning, we adopt an encoder-decoder structure to reconstruct the video from its temporal-masked frames. Particularly, we find that a higher masking ratio helps video understanding. Besides, we fully exploit the similarity relationship between videos by maximizing agreement between two augmented views of a video, which contributes to more discriminative and robust hash codes. Extensive experiments on three large-scale video datasets (i.e., FCVID, ActivityNet and YFCC) indicate that ConMH achieves state-of-the-art results. Code is available at https://github.com/huangmozhi9527/ConMH.

Deep Hybrid Neural Network With Attention Mechanism for Video Hash Retrieval Method

Deep Hybrid Neural Network With Attention Mechanism for Video Hash Retrieval Method

RTIM Hashing: Robust and Compact Video Hashing With a Rotation- and Translation-Invariant Model

Abstract Video hashing is a popular research topic in the fields of multimedia information and security because its fast matching and low-cost storage characteristics are widely used in many applications (video copy detection, video retrieval, video authentication, etc.). This paper describes a compact video hashing method with a rotation- and translation-invariant model (RTIM). The key contribution of this approach is that it innovatively reconstructs an input video into a 3D RTIM by combining ring partition and a pipeline histogram; this is a first in video hashing and helps make video hashes resistant to rotation and translation. Then, the proposed model is decomposed via Tucker decomposition, and the generated core tensor is used to produce a compact hash. As the core tensor is a compressed version of the original tensor, hash construction with the core tensor makes RTIM hashing compact and achieves desirable discrimination ability. Different from existing video hashing algorithms, RTIM hashing can not only resist many commonly used digital operations, especially video rotation and cyclic frame shifting, but also achieve good discrimination ability. Various experiments demonstrate the effectiveness of our algorithm. Receiver operating characteristic curve comparisons show that compared with the state-of-the-art video hashing algorithms, RTIM hashing is more robust and compact.

Practical protection against video data leakage via universal adversarial head

While online video sharing becomes more popular, it also causes unconscious leakage of personal information in the video retrieval systems like deep hashing. A snoop can collect more users’ private information from the video database by querying similar videos. This paper focuses on bypassing the deep video hashing based retrieval to prevent information from being maliciously collected. We propose universal adversarial head (UAH), which crafts adversarial query videos by prepending the original videos with a sequence of adversarial frames to perturb the normal hash codes in the Hamming space. This adversarial head can be generated only with a few natural videos, and mislead the retrieval system to return irrelevant videos when it is applied to most query videos. Furthermore, to obey the principle of information protection, we expand the proposed method to a data-free paradigm to generate the UAH, without access to users’ original videos. Extensive experiments demonstrate the effectiveness of our method in misleading deep video hashing under both white-box and black-box settings.

Watermarked Hashing As a Video Content Authentication Technique

The Authentication is the primary problem of cyber security as a dimension. It is just a method of approving a digital proof of identification. Different levels of access or completion of tasks are granted to different individuals, depending on who has signed up for the task at hand. There are many different types of authentication, ranging from passwords to fingerprints to bio metrics. These technologies add a layer of protection and prevent security lapses like data infringements. Digital video authentication is a method of evaluating whether the recorded video is genuine and has not been tampered with. Video hashing is commonly deployed in closer detection, video authentication and video tamper detection and a look for anti-piracy. Perceptual hashing has emerged as a powerful tool for verifying the validity of multimedia content and it continues to pique the interest of experts working in analyzing multimedia domains and spotting illegal or counterfeit videos on the internet.

Deep multi-query video retrieval

Video retrieval methods have been developed for a single query. Multi-query video retrieval problem has not been investigated yet. In this study, an efficient and fast multi-query video retrieval framework is developed. Query videos are assumed to be related to more than one semantic. The framework supports an arbitrary number of video queries. The method is built upon using binary video hash codes. As a result, it is fast and requires a lower storage space. Database and query hash codes are generated by a deep hashing method that not only generates hash codes but also predicts query labels when they are chosen outside the database. The retrieval is based on the Pareto front multi-objective optimization method. Re-ranking performed on the retrieved videos by using non-binary deep features increases the retrieval accuracy considerably. Simulations carried out on two multi-label video databases show that the proposed method is efficient and fast in terms of retrieval accuracy and time.

Structure-Adaptive Neighborhood Preserving Hashing for Scalable Video Search

In this paper, we propose a Structure-adaptive Neighborhood Preserving Hashing (SNPH) method for unsupervised scalable video search. Unlike most existing hashing methods which equally encode an entire video into a binary feature vector, we propose a neighborhood attention mechanism which encodes the neighborhood-relevant content of a video to better preserve the neighborhood relationships among videos. Motivated by the fact that a video usually contains multiple shots and each shot depicts a different activity, we further develop a structure-adaptive encoder to model the hierarchical structure of the video. Specifically, the encoder adaptively divides each video into multiple segments via detecting temporal boundaries across frames and encodes these segments as a compact binary vector to capture rich structural information. We integrate the neighborhood attention mechanism into the structure-adaptive encoder to learn hash functions that jointly preserve the neighborhood relationships among videos and exploit the hierarchical structure in a video. Experimental results on three widely used benchmark datasets show that our proposed method consistently outperforms state-of-the-art unsupervised video hashing methods.

Perceptual hashing method for video content authentication with maximized robustness

Perceptual video hashing represents video perceptual content by compact hash. The binary hash is sensitive to content distortion manipulations, but robust to perceptual content preserving operations. Currently, boundary between sensitivity and robustness is often ambiguous and it is decided by an empirically defined threshold. This may result in large false positive rates when received video is to be judged similar or dissimilar in some circumstances, e.g., video content authentication. In this paper, we propose a novel perceptual hashing method for video content authentication based on maximized robustness. The developed idea of maximized robustness means that robustness is maximized on condition that security requirement of hash is first met. We formulate the video hashing as a constrained optimization problem, in which coefficients of features offset and robustness are to be learned. Then we adopt a stochastic optimization method to solve the optimization. Experimental results show that the proposed hashing is quite suitable for video content authentication in terms of security and robustness.

Efficient video hashing based on low‐rank frames

Video hashing is a useful technology for diverse video applications, such as digital watermarking, copy detection and content authentication. This paper proposes a novel efficient video hashing based on low-rank frames. A key contribution is the low-rank frame calculation using the low-rank approximation of singular value decomposition (SVD). As the large singular values of SVD are stable to digital operations, video hash extraction using low-rank frames can provide good robustness. Since most energy is contained within the large singular values, low-rank frames also contribute to discrimination. Moreover, two-dimensional discrete wavelet transform (DWT) is applied to every low-rank frame and the mean of low-frequency DWT coefficients is selected as a hash element. Since these coefficients can represent input data approximately, hash discrimination is thus ensured. Experiments with 16,850 videos are carried out to test performances of the proposed algorithm. The results show that the proposed algorithm outperforms some well-known video hashing algorithms in computational time and classification about discrimination and robustness.

Video hashing with secondary frames and invariant moments

Video hashing is a useful technique of many multimedia systems, such as video copy detection, video authentication, tampering localization, video retrieval, and anti-privacy search. In this paper, we propose a novel video hashing with secondary frames and invariant moments. An important contribution is the secondary frame construction with 3D discrete wavelet transform, which can reach initial data compression and robustness against noise and compression. In addition, since invariant moments are robust and discriminative features, hash generation based on invariant moments extracted from secondary frames can ensure good classification of the proposed video hashing. Extensive experiments on 8300 videos are conducted to validate efficiency of the proposed video hashing. The results show that the proposed video hashing can resist many digital operations and has good discrimination. Performance comparisons with some state-of-the-art algorithms illustrate that the proposed video hashing outperforms the compared algorithms in classification in terms of receiver operating characteristic results.

Classification-enhancement deep hashing for large-scale video retrieval

With the explosive growth of video data on the Internet, retrieving and detecting similar video contents effectively has become a challenging problem. Whereas hashing is a mature technique for dealing with this problem, especially in image retrieval, when hashing techniques are applied to videos, a large gap in performance is always encountered compared with that of image hashing methods because of the complicated structures of videos. Generally, existing video hashing methods just directly apply image hashing approaches into video frames without considering temporal structure, leading to low performance in video retrieval. In this study, we proposed a video hashing method, called classification-enhancement deep hashing (CEDH), for large-scale video searches. The proposed CEDH first fuses the spatial–temporal information of videos in a deep end-to-end hashing network, and then leverages both neighborhood structure of semantics and triple similarity information to learn video hash codes. Subsequently, to enhance the precision of the hash codes during hash learning, a classification module is added after the fully connected layer of the deep network. We also use an additional code constraint to make the hash codes more suitable for containing sufficient information. Extensive experiments on three real-world large-scale video datasets show that our proposed method significantly outperforms state-of-the-art algorithms.

Robust Video Hashing Based on Multidimensional Scaling and Ordinal Measures

Multimedia hashing is a useful technology of multimedia management, e.g., multimedia search and multimedia security. This paper proposes a robust multimedia hashing for processing videos. The proposed video hashing constructs a high-dimensional matrix via gradient features in the discrete wavelet transform (DWT) domain of preprocessed video, learns low-dimensional features from high-dimensional matrix via multidimensional scaling, and calculates video hash by ordinal measures of the learned low-dimensional features. Extensive experiments on 8300 videos are performed to examine the proposed video hashing. Performance comparisons reveal that the proposed scheme is better than several state-of-the-art schemes in balancing the performances of robustness and discrimination.

A Supervised Video Hashing Method Based on a Deep 3D Convolutional Neural Network for Large-Scale Video Retrieval.

Recently, with the popularization of camera tools such as mobile phones and the rise of various short video platforms, a lot of videos are being uploaded to the Internet at all times, for which a video retrieval system with fast retrieval speed and high precision is very necessary. Therefore, content-based video retrieval (CBVR) has aroused the interest of many researchers. A typical CBVR system mainly contains the following two essential parts: video feature extraction and similarity comparison. Feature extraction of video is very challenging, previous video retrieval methods are mostly based on extracting features from single video frames, while resulting the loss of temporal information in the videos. Hashing methods are extensively used in multimedia information retrieval due to its retrieval efficiency, but most of them are currently only applied to image retrieval. In order to solve these problems in video retrieval, we build an end-to-end framework called deep supervised video hashing (DSVH), which employs a 3D convolutional neural network (CNN) to obtain spatial-temporal features of videos, then train a set of hash functions by supervised hashing to transfer the video features into binary space and get the compact binary codes of videos. Finally, we use triplet loss for network training. We conduct a lot of experiments on three public video datasets UCF-101, JHMDB and HMDB-51, and the results show that the proposed method has advantages over many state-of-the-art video retrieval methods. Compared with the DVH method, the mAP value of UCF-101 dataset is improved by 9.3%, and the minimum improvement on JHMDB dataset is also increased by 0.3%. At the same time, we also demonstrate the stability of the algorithm in the HMDB-51 dataset.

Discriminative Codebook Hashing for Supervised Video Retrieval.

In recent years, hashing learning has received increasing attention in supervised video retrieval. However, most existing supervised video hashing approaches design hash functions based on pairwise similarity or triple relationships and focus on local information, which results in low retrieval accuracy. In this work, we propose a novel supervised framework called discriminative codebook hashing (DCH) for large-scale video retrieval. The proposed DCH encourages samples within the same category to converge to the same code word and maximizes the mutual distances among different categories. Specifically, we first propose the discriminative codebook via a predefined distance among intercode words and Bernoulli distributions to handle each hash bit. Then, we use the composite Kullback–Leibler (KL) divergence to align the neighborhood structures between the high-dimensional space and the Hamming space. The proposed DCH is optimized via the gradient descent algorithm. Experimental results on three widely used video datasets verify that our proposed DCH performs better than several state-of-the-art methods.