A Novel Video Encryption Method Based on Faster R-CNN

Duan, Lijuan; Zhang, Dongkui; Xu, Fan; Cui, Guoqin

doi:10.2991/csece-18.2018.21

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Furthermore, research studies on ROI encryption in HEVC/H.265 are rare, so it is challenging to find relevant evidence. Additionally, we found some studies [8], [22], [23], but these deal with compression-independent encryption types and cannot be evaluated because no corresponding comparative attribute is provided, such as the encryption method or unit. In conclusion, this study proposed a novel ROI encryption conducted at HEVC/H.265 video processing unit level, and it is the first attempt as far as we know.…”

Section: B Roi Encryption In Hevc/h265mentioning

confidence: 99%

“…One is compressionindependent encryption, which may not rely on the video codec and can be applied to most video systems [7]. However, independent execution may result in a format compliance issue, and it is not generally used for real-time video processing because it delays the overall process [8]. In contrast, joint compression and encryption meet these requirements while maintaining a lower rate of quality loss in a video [9].…”

Section: Introductionmentioning

confidence: 99%

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Coding Unit-Based Region of Interest Encryption in HEVC/H.265 Video

Kim

2023

IEEE Access

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The region of interest (ROI) encryption in the video can reduce the complexity of calculation and improve encryption speed by encrypting only the area containing critical visual information. Above all, ROI encryption can expand the utilization domain (e.g., video surveillance), unlike general encryption methods that de-identify the entire frame. However, the traditional ROI encryption process in high-efficiency video coding (HEVC)/H.265 is more complex than in advanced video coding/H.264, and the encrypted area tends to be wider than the ROI. Thus, cryptographic algorithms are applied outside the ROI, which wastes computing resources and restricts the visual information that needs to be provided. Therefore, this paper proposes a coding unit (CU)-based ROI encryption for HEVC/H.265 video. The proposed method selectively encrypts HEVC/H.265 parameters, such as the intra prediction mode, motion vector (MV), MV sign, transform coefficient (TC), and TC sign, which have significant visual influence, and adopts the tile concept for parallel processing frames. The CU-based ROI encryption reduces complexity by identifying the encryption area based on the CU coordinates, applying encryption only to the CUs associated with the ROI. This approach can preserve the area around the ROI by restricting the reference area. Moreover, it provides up to about 30% faster encryption speed than the traditional method while maintaining performance (i.e., peak signal-to-noise ratio and structural similarity index measure).

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Section: B Roi Encryption In Hevc/h265mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coding Unit-Based Region of Interest Encryption in HEVC/H.265 Video

Kim

2023

IEEE Access

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“…The associated limitations of this method were increasing the key complexity and processing time for encryption. In [31], the region of interest (ROI) based on faster region based convolutional neural networks (R-CNN) method for video encryption was presented to overcome the shortcoming of several video encryption schemes. Different encryption schemes had been used to encrypt the non-ROI in order to implement fully video frame encryption.…”

Section: Related Workmentioning

confidence: 99%

Novel lightweight video encryption method based on ChaCha20 stream cipher and hybrid chaotic map

Maolood

Gbashi

Mahmood

2022

IJECE

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<span lang="EN-US">In the recent years, an increasing demand for securing visual resource-constrained devices become a challenging problem due to the characteristics of these devices. Visual resource-constrained devices are suffered from limited storage space and lower power for computation such as wireless sensors, internet protocol (IP) camera and smart cards. Consequently, to support and preserve the video privacy in video surveillance system, lightweight security methods are required instead of the existing traditional encryption methods. In this paper, a new light weight stream cipher method is presented and investigated for video encryption based on hybrid chaotic map and ChaCha20 algorithm. Two chaotic maps are employed for keys generation process in order to achieve permutation and encryption tasks, respectively. The frames sequences are encrypted-decrypted based on symmetric scheme with assist of ChaCha20 algorithm. The proposed lightweight stream cipher method has been tested on several video samples to confirm suitability and validation in term of encryption–decryption procedures. The performance evaluation metrics include visual test, histogram analysis, information entropy, correlation analysis and differential analysis. From the experimental results, the proposed lightweight encryption method exhibited a higher security with lower computation time compared with state-of-the-art encryption methods.</span>

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“…CNNs have also been used to predict tropical cyclone intensity precisely using satellite imagery and hailstorm detection in radar images with higher accuracy than previous techniques. After instantiation of CNN's layer architecture, the model is trained with truth data sets, which develops expertise to predict proper characteristics in the image [4]. While the training of a CNN can take a long time, the forecasts are rapid compared to older algorithms or a manual approach.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Autoencoder-Based Deep Learning Approach for Aerosol Emission Detection Using LiDAR Dataset

et al. 2022

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Quantifying atmospheric aerosols and their linkages to climatic repercussions is necessary to understand the dynamics of climate forcing and enhance our knowledge of climate change. Because of this reactivity to precipitation, temperature, topography, and human activity, the atmospheric boundary layer (ABL) is one of the most dynamic atmospheric regions: ABL aerosols have a big impact on the evolution of climate change’s radiative forcing, human health, food security, and, eventually, the local and global economy. Continuous monitoring and instrumental and computational approaches are required for the detection and analysis of ABL pattern behavior. This paper provides a deep learning-based outer layer aerosol detection system based on Light Detection and Ranging (LiDAR) data fusion. The suggested method applies sequential models to turn low-level data into compressed features using object-based analysis, feature-level fusion, and autoencoder-based dimensionality reduction. Convolutional neural networks (CNNs) were used to convert compressed data into high-level properties that could be used to categorize air particles in the outer layer. This research describes deep learning approaches that allowed for detecting 40% more atmospheric features at a horizontal resolution of 5 km during daytime operations when applied to LiDAR data. Compared to existing deep learning algorithms for edges and complicated near-surface sceneries during the day, a convolutional autoencoder (CAE) trained using LiDAR dataset standard data products showed the potential for improved aerosol discrimination with 98% accuracy.

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A Novel Video Encryption Method Based on Faster R-CNN

Cited by 5 publications

References 10 publications

Coding Unit-Based Region of Interest Encryption in HEVC/H.265 Video

Coding Unit-Based Region of Interest Encryption in HEVC/H.265 Video

Novel lightweight video encryption method based on ChaCha20 stream cipher and hybrid chaotic map

Convolutional Autoencoder-Based Deep Learning Approach for Aerosol Emission Detection Using LiDAR Dataset

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