A secure video steganography scheme using DWT based on object tracking

Dalal, Mukesh; Juneja, Mamta

doi:10.1080/19393555.2021.1896055

Cited by 7 publications

(3 citation statements)

References 38 publications

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“…Deep Learning-Based Image Steganography. Compared with traditional methods, image steganography based on deep learning has attracted extensive attention due to its improved performance and more flexible application scenarios [17,18]. Qian firstly proposed a novel Gaussian neuron CNN (GNCNN) model in 2015 [19] for secret data hiding, which processed the image by using KV kernel of traditional algorithm and CNN network.…”

Section: 2mentioning

confidence: 99%

“…e encoder network generates a stego image by embedding a secret image into Y channel of a cover image; the decoder network extracts the secret image from the stylized stego image. Similar to the model in [18], the architecture of encoder/decoder network is composed of five convolutional layers with 50 convolution kernels of {3 × 3, 4 × 4, 5 × 5}, as shown in Figure 2.…”

Section: The Improved Image Steganography Framework For Neural Style Transfermentioning

confidence: 99%

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An Improved Image Steganography Framework Based on Y Channel Information for Neural Style Transfer

Lin

Zhu

et al. 2022

Security and Communication Networks

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Neural style transfer has effectively assisted artistic design in recent years, but it has also accelerated the tampering, synthesis, and dissemination of a large number of digital image resources without permission, resulting in a large number of copyright disputes. Image steganography can hide secret information in cover images to realize copyright protection, but the existing methods have poor robustness, which is hard to extract the original secret information from stylized steganographic (stego) images. To solve the above problem, we propose an improved image steganography framework for neural style transfer based on Y channel information and a novel structural loss, composed of an encoder, a style transfer network, and a decoder. By introducing a structural loss to restrain the process of network training, the encoder can embed the gray-scale secret image into Y channel of the cover image and then generate steganographic image, while the decoder can directly extract the above secret image from a stylized stego image output by the style transfer network. The experimental results demonstrate that the proposed method can effectively recover the original secret information from the stylized stego image, and the PSNR of the extracted secret image and the original secret image can reach 23.4 and 27.29 for the gray-scale secret image and binary image with the size of 256×256, respectively, maintaining most of the details and semantics. Therefore, the proposed method can not only preserve most of the secret information embedded in a stego image during the stylization process, but also help to further hide secret information and avoid steganographic attacks to a certain extent due to the stylization of a stego image, thus protecting secret information like copyright.

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Section: 2mentioning

confidence: 99%

Section: The Improved Image Steganography Framework For Neural Style Transfermentioning

confidence: 99%

An Improved Image Steganography Framework Based on Y Channel Information for Neural Style Transfer

Lin

Zhu

et al. 2022

Security and Communication Networks

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show abstract

“…Most of these methods are based on analyzing pixels in frames. These include histograms [14,15], edge change [16,17], velocity vectors [18][19][20][21], DCT and DWT methods [22,23], and others. The obtained features allow us to combine frames that will collectively make up a scene in a video.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Maintenance Scenarios for Equipment and Control Systems in Industry

Koteleva,

Valnev

2023

Applied Sciences

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The well-known methods of scene extraction on video are focused on analyzing the similarity between frames. However, they do not all analyze the composition of the image scene, which may remain the same during maintenance. Therefore, this paper proposes an algorithm for equipment maintenance scene detection based on human hand tracking. It is based on the assumption that, when servicing technological equipment, it is possible to determine the change in repair action by the position of the service engineer’s hands. Thus, certain information and the algorithm that processes these changes allow us to segment the video into actions performed during the service. We process the time series obtained by moving the hand position using spectral singular value decomposition for multivariate time series. To verify the algorithm, we performed maintenance on the control cabinet of a mining conveyor and recorded the work on a first-person video, which was processed using the developed method. As a result, we obtained some scenes corresponding to opening the control cabinet, de-energizing the unit, and checking the contacts with a multimeter buzzer test. A third-person video of motor service was similarly processed. The algorithm demonstrated the results in separate scenes of removing screws, working with a multimeter, and disconnecting and replacing motor parts.

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Robust digital image watermarking using cuckoo search optimization and probabilistic neural network

Gupta,

Kishore

2023

Multimed Tools Appl

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A secure video steganography scheme using DWT based on object tracking

Cited by 7 publications

References 38 publications

An Improved Image Steganography Framework Based on Y Channel Information for Neural Style Transfer

An Improved Image Steganography Framework Based on Y Channel Information for Neural Style Transfer

Automatic Detection of Maintenance Scenarios for Equipment and Control Systems in Industry

Robust digital image watermarking using cuckoo search optimization and probabilistic neural network

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