Robust Video Research Articles

Rate Adaptation for Robust and Low-Latency Video Transmissions Using Multi-AP Wireless LAN

Rate Adaptation for Robust and Low-Latency Video Transmissions Using Multi-AP Wireless LAN

Adaptive QIM With Minimum Embedding Cost for Robust Video Steganography on Social Networks

Sharing videos on social networks has become more and more popular, which provides a new scenario for covert communication. Video sharing-based hidden communication can conceal the contact relation between the sender and receiver and achieve the one-to-many delivery of secret messages. However, most video steganographic methods are unable to complete reliable hidden communication on social networks because of lossy video recompression. In this paper, we propose adaptive QIM (Quantization Index Modulation) with minimum embedding cost, which decreases the quantization distortion as much as possible to improve the security performance against steganalysis under the same level of robustness. Furthermore, based on the proposed quantization modulation scheme, we implement two robust steganographic methods in the DWT-SVD domain and the DTCWT-SVD domain. Experimental results show that the overall performance of our proposed modulation scheme outperforms QIM and adaptive QIM. Compared with existing robust video data hiding, two proposed steganographic methods demonstrate superior robustness and security on local lossy channels and social networks.

Chaos Based Encryption Technique for Compressed H264/AVC Videos

Saving videos streaming from the unlawful users became an urgent claim. Cryptography and Steganography are the most important and indispensable processes to protect and embed the multimedia data during the transfer or storage process. Chaotic-based cryptography has been proposed to be an efficient encryption technique for multimedia applications and has played a vital role in information security. While the chaotic maps have a long history of researching as a nonlinear dynamic system, visual encryption is useful for concealing messages in videos and safeguarding private content from unauthorized intrusion. In this paper, two new proposed algorithms for compressed videos by the advanced H.264/AVC video coding are presented. First algorithm approach has been implemented by robust video encryption algorithm based on the chaos maps with random key to be tested under different attacks. The second algorithm approach has been implemented by using the hybrid of both steganography and cryptography based on chaotic maps for video frames. The proposed algorithms are conducted on luminance component Y of a set of different YUV video sequences with different resolution using MATLAB software. The simulation results of the proposed algorithms are evaluated using various performance metrics comparing to that with the state-of-art techniques. The suggested approaches have shown to be more resistant to many sorts of attacks and is recommended for future using in real-time applications.

Weakly-Supervised Video Object Grounding via Causal Intervention.

We target at the task of weakly-supervised video object grounding (WSVOG), where only video-sentence annotations are available during model learning. It aims to localize objects described in the sentence to visual regions in the video, which is a fundamental capability needed in pattern analysis and machine learning. Despite the recent progress, existing methods all suffer from the severe problem of spurious association, which will harm the grounding performance. In this paper, we start from the definition of WSVOG and pinpoint the spurious association from two aspects: (1) the association itself is not object-relevant but extremely ambiguous due to weak supervision; and (2) the association is unavoidably confounded by the observational bias when taking the statistics-based matching strategy in existing methods. With this in mind, we design a unified causal framework to learn the deconfounded object-relevant association for more accurate and robust video object grounding. Specifically, we learn the object-relevant association by causal intervention from the perspective of video data generation process. To overcome the problems of lacking fine-grained supervision in terms of intervention, we propose a novel spatial-temporal adversarial contrastive learning paradigm. To further remove the accompanying confounding effect within the object-relevant association, we pursue the true causality by conducting causal intervention via backdoor adjustment. Finally, the deconfounded object-relevant association is learned and optimized under a unified causal framework in an end-to-end manner. Extensive experiments on both IID and OOD testing sets of three benchmarks demonstrate its accurate and robust grounding performance against state-of-the-arts.

A Robust Video Watermarking Scheme with Squirrel Search Algorithm

Advancement in multimedia technology has resulted in protection against distortion, modification, and piracy. For implementing such protection, we have an existing technique called watermarking but obtaining desired distortion level with sufficient robustness is a challenging task for watermarking in multimedia applications. In the paper, we proposed a smart technique for video watermarking associating meta-heuristic algorithms along with an embedding method to gain an optimized efficiency. The main aim of the optimization algorithm is to obtain solutions with maximum robustness, and which should not exceed the set threshold of quality. To represent the accuracy of the proposed scheme, we employ a popular video watermarking technique (DCT domain) having frame selection and embedding method for watermarking. A squirrel search algorithm is chosen as a meta-heuristic algorithm that utilizes the stated fitness function. The results indicate that quality constraint is fulfilled, and the proposed technique gives improved robustness against different attacks with several quality thresholds. The proposed technique could be practically implemented in several multimedia applications such as the films industry, medical imagery, OOT platforms, etc.

MPEG and DA-AD Resilient DCT-Based Video Watermarking Using Adaptive Frame Selection

We present a robust video watermarking scheme and report the detailed robustness of the video watermarking assessed based on standard criteria obtained from Information Hiding and its Criteria (IHC) Committee. Using discrete cosine transform domain spread-spectrum watermarking, our system achieves robustness under various non-hostile video processing techniques, including MPEG compression and digital/analog–analog/digital (DA-AD) conversion. The proposed system ensures that a 16-bit embedded sequence can be extracted through adaptive frame selection in any 15-s interval, even with a long video clip. To evaluate the performance of the proposed watermarking scheme, we conducted robustness tests under a DA-AD conversion environment, based on the MPEG-4 part 10 (H.264) codec. The experiment results obtained indicate that, in addition to being robust against non-hostile video processes, the proposed method achieves invisibility. The assessment of the developed watermarking scheme also satisfies the third edition of the IHC video watermarking evaluation criteria.

A COMPARATIVE STUDY FOR VIDEO CLASSIFICATION TECHNIQUES USING DIRECT FEATURES MATCHING, MACHINE LEARNING, AND DEEP LEARNING

Videos are considered the new era communication language between internet users due to the explosion of smart-phones usage and increase in internet bandwidth and storage space. This has fueled the need to develop robust video analysis techniques. Specifically, video classification presents a unique task for field researchers, as it has numerous critical applications, such as video indexing, searching, annotation and surveillance. Videos inherently embody static and dynamic information that is encoded in frames. The task is further prioritized due to the gigantic amounts of available videos in the digital world, which requires a robust way to organize these videos. Throughout literature, researchers have generally adopted three main techniques to classify videos, i.e., direct features matching, machine learning-based methods, and deep learning-based methods. Each of these methods is suitable for a specific application type. This paper is designed to assess which of the three common working approaches are better for video classification. Furthermore, the paper aims to examine whether and how these methods affect/improve video classification performance and key factors to constructing a robust video classification system. This novel research paper covers an important research gap by introducing a rigorous comparative analysis of the three methods highlighting their advantages and disadvantages and guiding field researchers. A comprehensive analysis brings the paper findings together using a benchmark group of challenging large-scale video datasets (~29k videos). This would provide field researchers with the necessary information to choose the best method for their video classification research work.

Protecting Ownership Rights of Videos Against Digital Piracy: An Efficient Digital Watermarking Scheme

Violation of one’s intellectual ownership rights by the others is a common problem which entertainment industry frequently faces now-a-days. Sharing of information over social media platforms such as Instagram, WhatsApp and twitter without giving credit the owner causes huge financial losses to the owner and hence needs an immediate attention. Digital watermarking is a promising technique to protect owners’ right against digital piracy. Most of the state-of-the-art techniques does not provides adequate level of resilience against majority of video specific attacks and other commonly applied attacks. Therefore, this paper proposes a highly transparent and robust video watermarking solution to protect the owners rights by first convert each video frame into YCbCr color components and then select twenty five strongest speeded-up robust features (SURF) points of the normalized luminance component as points for both watermark embedding and extraction. After applying variety of geometric, simple signal processing and video specific attacks on the watermarked video meticulous analysis is performed using popular metrics which reveals that the proposed scheme possesses high correlation value which makes it superior for practical applications against these attacks. The scheme also proposes a novel three-level impairment scale for subjective analysis which gives stable results to derive correct conclusions.

A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection

Fire is an abnormal event that can cause significant damage to lives and property. Deep learning approach has made large progress in vision-based fire detection. However, there is still the problem of false detections due to the objects which have similar fire-like visual properties such as colors or textures. In the previous video-based approach, Faster Region-based Convolutional Neural Network (R-CNN) is used to detect the suspected regions of fire (SRoFs), and long short-term memory (LSTM) accumulates the local features within the bounding boxes to decide a fire in a short-term period. Then, majority voting of the short-term decisions is taken to make the decision reliable in a long-term period. To ensure that the final fire decision is more robust, however, this paper proposes to use a Bayesian network to fuse various types of information. Because there are so many types of Bayesian network according to the situations or domains where the fire detection is needed, we construct a simple Bayesian network as an example which combines environmental information (e.g., humidity) with visual information including the results of location recognition and smoke detection, and long-term video-based majority voting. Our experiments show that the Bayesian network successfully improves the fire detection accuracy when compared against the previous video-based method and the state of art performance has been achieved with a public dataset. The proposed method also reduces the latency for perfect fire decisions, as compared with the previous video-based method.

Machine learning-assisted imaging analysis of a human epiblast model.

The human embryo is a complex structure that emerges and develops as a result of cell-level decisions guided by both intrinsic genetic programs and cell-cell interactions. Given limited accessibility and associated ethical constraints of human embryonic tissue samples, researchers have turned to the use of human stem cells to generate embryo models to study specific embryogenic developmental steps. However, to study complex self-organizing developmental events using embryo models, there is a need for computational and imaging tools for detailed characterization of cell-level dynamics at the single cell level. In this work, we obtained live cell imaging data from a human pluripotent stem cell (hPSC)-based epiblast model that can recapitulate the lumenal epiblast cyst formation soon after implantation of the human blastocyst. By processing imaging data with a Python pipeline that incorporates both cell tracking and event recognition with the use of a CNN-LSTM machine learning model, we obtained detailed temporal information of changes in cell state and neighborhood during the dynamic growth and morphogenesis of lumenal hPSC cysts. The use of this tool combined with reporter lines for cell types of interest will drive future mechanistic studies of hPSC fate specification in embryo models and will advance our understanding of how cell-level decisions lead to global organization and emergent phenomena. Insight, innovation, integration: Human pluripotent stem cells (hPSCs) have been successfully used to model and understand cellular events that take place during human embryogenesis. Understanding how cell-cell and cell-environment interactions guide cell actions within a hPSC-based embryo model is a key step in elucidating the mechanisms driving system-level embryonic patterning and growth. In this work, we present a robust video analysis pipeline that incorporates the use of machine learning methods to fully characterize the process of hPSC self-organization into lumenal cysts to mimic the lumenal epiblast cyst formation soon after implantation of the human blastocyst. This pipeline will be a useful tool for understanding cellular mechanisms underlying key embryogenic events in embryo models.

15‐1: High Resolution Display Data Transmission using Weak Sub‐color DWT

As a resolution of display increases, it is necessary to process video data more efficiently and economically. In this paper, an advanced technique is proposed to reduce its data bandwidth in real time without any noticeable degradation in its image quality. By analyzing the frequency characteristics of an input image data with respect to its sub‐colors, a sub‐color data having relatively smaller proportion in high frequency which can be regarded as a ‘Weak Sub‐color data’ can be converted and reduced using the Discrete Wavelet Transformation (DWT). Some of the DWT‐transformed data is transmitted with the other non‐processed original sub‐color data to reduce its data size. This technique can provide a robust video system interface that reduces its transmission bandwidth up to 25% or more with improved image quality.

Development of payload capacity enhanced robust video watermarking scheme based on symmetry of circle using lifting wavelet transform and SURF

Using simple image and video editing software it is relatively easy to pirate the digital contents at the cost of huge revenue loss to the content owner. Entertainment is the key industry that is overwhelmed by huge financial loss due to digital piracy. WhatsApp, Facebook, Instagram, Pinterest and Twitter are some of the massive social media platforms that are used by millions of users. Many of the users share digital content without giving credit to the owner. Digital watermarking is an efficient technique to protect the digital rights and legal compensation of content owners. Due to the poor selection of watermarking locations, most of the state-of-art methods were fall short to preserve the relationships among the host data. Hence such methods were not efficient to provide safeguards against digital piracy. Therefore, this paper proposes a digital video watermarking technique that analyzes, extracts and processes the useful patterns or features of data based on the symmetry of the circle using 2D-Lifting Wavelet Transform (2D-LWT) and Speeded-Up Robust Features (SURF). SURF method is used to obtain five invariant feature points on an approximation part of 2D-LWT utilizing the luminance component of each video frame. The invariant points serve as centers of the circles used to find the embed locations. The maximum intensity value point in all the points on the circumference of the 1/8 part of the circle is then calculated to serve as an embedding location. Three other points in different quadrants are simply obtained using the concept of symmetry of the circle. After applying temporal desynchronization, compression and insertion of noise attacks the video is assessed through subjective and objective evaluation metrics. The results reveal a high degree of correlation (close to one) and good visual transparency which proves that the proposed scheme is superior for practical applications.

An Adaptive Fuzzy based FEC Algorithm for Robust Video Transmission over Wireless Networks

Forward Error Correction (FEC) is a commonly adopted mechanism to mitigate packet loss/bit error during real-time communication. An adaptive, Fuzzy based FEC algorithm to provide a robust video quality metric for multimedia transmission over wireless networks has been proposed to optimize the redundancy of the generated code words from a Reed-Solomon encoder and to save the bandwidth of the network channel. The scheme is based on probability estimations derived from the data loss rates related to the recovery mechanism at the client end. By applying the adaptive FEC, the server uses the reports to predict the next network loss rate using a curve-fitting technique to generate the optimized number of redundant packets to meet specific residual error rates at the client end. Simulation results in the cellular system show that the video quality is massively adapted to the optimized FEC codes based on the probability of packet loss and packet correlation in a wireless environment.

STA3D: Spatiotemporally attentive 3D network for video saliency prediction

• Attention guiding is significant for video saliency prediction based on 3D CNN. • A spatiotemporally attentive 3D CNN for robust video saliency prediction is proposed. • An adaptive upsampling module for refining spatial features is proposed. • A frame-wise attention module for propagating temporal features is proposed. • The effectiveness of the proposed method is comprehensively evaluated. 3D fully convolutional networks (FCN), which jointly leverage the spatial and temporal cues, have achieved great success in video saliency prediction. However, they still have limitations in some challenging cases, e.g. fixation shift. To address this issue, we propose a SpatioTemporally Attentive 3D Network (STA3D) to selectively propagate the significant temporal features and refine the spatial features in 3D FCN for video saliency prediction. Extensive experiments on three standard datasets demonstrate the superiority of the proposed model against the state-of-the-art.

An efficient DCT-SBPM based video steganography in compressed domain

In this research work, an efficient robust video steganography in compressed domain is presented based on discrete cosine transform (DCT) encoded with H.264 codec. In this method, a compressed video (.mp4) of different resolutions, frame rates and sizes are used as a cover and RGB image is used as a secret message. Using appropriate stego key, the secret frames from the sequence of video frames are selected and the non-dynamic pixels of these frames are identified. The least significant bits (LSB) of the DCT component of these non-dynamic pixels are used as carrier object for hiding the R, G, and B components of secret message individually. An appropriate embedding key is designed to embed secret message in cover video such that the quality assessment parameters viz. Peak signal-to-noise ratio (PSNR) and mean square error (MSE) of imperceptibility can be optimized. Also, the successful extraction of secret message from the cover stego video is assessed by the robustness parameters viz. Bit error rate (BER) and similarity (Sim). The hiding capacity of proposed video steganography with respect to secret message and cover frame is measured by using hiding ratio (HR). The efficiency of proposed video steganography method is compared with the reported methodologies on the basis of the parameters of imperceptibility and robustness.

IoT-based smart healthcare video surveillance system using edge computing

Managing distributed smart surveillance system is identified as a major challenging issue due to its comprehensive aggregation and analysis of video information on the cloud. In smart healthcare applications, remote patient and elderly people monitoring require a robust response and alarm alerts from surveillance systems within the available bandwidth. In order to make a robust video surveillance system, there is a need for fast response and fast data analytics among connected devices deployed in a real-time cloud environment. Therefore, the proposed research work introduces the Cloud-based Object Tracking and Behavior Identification System (COTBIS) that can incorporate the edge computing capability framework in the gateway level. It is an emerging research area of the Internet of Things (IoT) that can bring robustness and intelligence in distributed video surveillance systems by minimizing network bandwidth and response time between wireless cameras and cloud servers. Further improvements are made by incorporating background subtraction and deep convolution neural network algorithms on moving objects to detect and classify abnormal falling activity monitoring using rank polling. Therefore, the proposed IoT-based smart healthcare video surveillance system using edge computing reduces the network bandwidth and response time and maximizes the fall behavior prediction accuracy significantly comparing to existing cloud-based video surveillance systems.

Chaos-based Video Steganography Method in Discrete Cosine Transform Domain

Due to the technological advancements in digital communication, the amount of multimedia content over the internet has increased manifold in past decade. This has renewed the internet of researchers in the area of privacy and secure communication. This paper presents a secure and robust video steganography method in discrete cosine transform (DCT) domain. In order to enhance the security of the proposed algorithm, the frame selection process is randomized and the secret data are pre-treated using Arnold’s cat map. The secret data are embedded in the middle band DCT coefficient using two pseudo random sequences. These sequences are generated using a chaotic map. We analyze the proposed algorithm in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), multi-scale structural similarity index (MSSIM) and video quality metric (VQM). The evaluation has been done on 107 video sequences. The experimental results demonstrate that the algorithm maintains acceptable video quality. The robustness of the proposed method is tested under Gaussian and salt and pepper noise attack using correlation between original and recovered images. The proposed algorithm is able to recover 90.60% data without error under salt and pepper noise ([Formula: see text]) attack and 87.23% data correctly under Gaussian noise attack with mean [Formula: see text] and variance [Formula: see text].

A secure and robust video steganography scheme for covert communication in H.264/AVC

The proposed scheme utilized H.264/AVC video format for steganography which is the most common video standard at present. The scheme employed Discrete Wavelet Transform (DWT) on the Region of Interest (ROI) based on multiple moving objects tracking. After tracking multiple objects, each object is embedded with different secret images to improve the capacity. Multiple object tracking helps in achieving robustness and security; in addition, secret data is encrypted before embedding to provide a high level of security. The proposed scheme is tested on numerous video sequences by evaluating both subjective and objective metrics. Different metrics employed for objective evaluation involves Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity (SSIM) Index, Normalized Cross Correlation (NCC) and Bit Error Rate (BER). However, subjective evaluation is carried out by visual inspection. Additionally, the proposed scheme has been tested against noise, compression, frame rate change and scaling attacks to ensure robustness. Further, the security performance has been analysed by testing against three existing steganalysis techniques and histogram analysis. The main aim of the paper is to focus on robustness and security without compromising hiding capacity and imperceptibility. The reckoning result proves not only high robustness and security but also improves imperceptibility and capacity.

Modified Rider Optimization-Based V Channel Magnification for Enhanced Video Super Resolution

In video Super Resolution (SR), the problem of cost expense concerning the attainment of enhanced spatial resolution, computational complexity and difficulties in motion blur makes video SR a complex task. Moreover, maintaining temporal consistency is crucial to achieving an efficient and robust video SR model. This paper plans to develop an intelligent SR model for video frames. Initially, the video frames in RGB format will be transformed into HSV. In general, the improvement in video frames is done in V-channel to achieve High-Resolution (HR) videos. In order to enhance the RGB pixels, the current window size is enhanced to high-dimensional window size. As a novelty, this paper intends to formulate a high-dimensional matrix with enriched pixel intensity in V-channel to produce enhanced HR video frames. Estimating the enriched pixels in the high-dimensional matrix is complex, however in this paper, it is dealt in a significant way by means of a certain process: (i) motion estimation (ii) cubic spline interpolation and deblurring or sharpening. As the main contribution, the cubic spline interpolation process is enhanced via optimization in terms of selecting the optimal resolution factor and different cubic spline parameters. For optimal tuning, this paper introduces a new modified algorithm, which is the modification of the Rider Optimization Algorithm (ROA) named Mean Fitness-ROA (MF-ROA). Once the HR image is attained, it combines the HSV and converts to RGB, which obtains the enhanced output RGB video frame. Finally, the performance of the proposed work is compared over other state-of-the-art models with respect to BRISQUE, SDME and ESSIM measures, and proves its superiority over other models.

A Compressed-Domain Robust Video Watermarking Against Recompression Attack

With the development of communication networks and the widespread use of mobile terminals, videos are increasingly shared and distributed among mobile users. During the process, the video is recompressed to a certain file size on the sending side and sent to the receiving side via the server. This makes robust video watermark with low complexity to resist recompression attacks become an important issue to address. The proposed video watermarking method in this article is specially designed for resisting recompression attacks when quantization parameter (QP) is greatly increased. In the proposed method, by using the texture information and motion information of video, the invariance of video content under different quantization parameters could be found to help improve the anti-recompression attack ability of video watermark. Moreover, the proposed framework does not use a location map which has security risk to locate the watermark. It aims at finding the optimal location of watermark embedding adaptively according to the feature of video content itself. The experiment results indicate that the proposed video watermarking method can not only achieve greater robustness against recompression attack but also effectively limit the degradation in video perceptual quality.