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Chinese Researchers Embed Secret Messages in Videos That Survive Social Media Distortionsby@kinetograph

Chinese Researchers Embed Secret Messages in Videos That Survive Social Media Distortions

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Kinetograph: The Video Editing Technology Publication

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December 25th, 2024
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Researchers developed a method to hide secret messages that is more secure and resistant to distortion during online sharing.
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STORY’S CREDIBILITY

Academic Research Paper

Academic Research Paper

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Authors:

(1) Xueying Mao, School of Computer Science, Fudan University, China (xymao22@m.@fudan.edu.cn);

(2) Xiaoxiao Hu, School of Computer Science, Fudan University, China (xxhu23@m.fudan.edu.cn);

(3) Wanli Peng, School of Computer Science, Fudan University, China (pengwanli@fudan.edu.cn);

(4) Zhenliang Gan, School of Computer Science, Fudan University, China (zlgan23@m.@fudan.edu.cn);

(5) Qichao Ying, School of Computer Science, Fudan University, China (qcying20@fudan.edu.cn);

(6) Zhenxing Qian, School of Computer Science, Fudan University, China and a Corresponding Author (zxqian@fudan.edu.cn);

(7) Sheng Li, School of Computer Science, Fudan University, China (lisheng@fudan.edu.cn);

(8) Xinpeng Zhang, School of Computer Science, Fudan University, China (zhangxinpeng@fudan.edu.cn).

Editor's note: This is Part 3 of 7 of a study describing the development of a new method to hide secret messages in semantic features of videos, making it more secure and resistant to distortion during online sharing. Read the rest below.

3. PROPOSED APPROACH

image

3.1. Information Encoding Module

image

3.2. Secret Message Embedding and Extraction Module

This module aims to embed the secret message during face swapping. The key problem is how to implement face swapping under the guidance of secret message. To our understanding, the latent features of the cover video encompass both identity and attribute feature. Face swapping essentially involves replacing the cover video’s identity with that of the reference image. Consequently, we embed the secret message into the identity feature of the reference image, formulated as follows:


image


where λ is a hyper-parameter adjusting the influence of secret message on identity feature.


image

3.3. Attacking Layer

To bolster the robustness of our method for face-swapping videos in real-world scenarios, we design a attacking layer. This module simulates prevalent distortions encountered across social network platforms.


JPEG Compression. JPEG compression involves a nondifferentiable quantization step due to rounding. To mitigate this, we apply Shin et al.’s method [53] to approximate the near-zero quantization step using function Eq. (6):


image


where x denotes pixels of the input image. We uniformly sample the JPEG quality from within the range of [50, 100].


Color Distortions. We consider two general color distortions: brightness and contrast. We perform a linear transformation on the pixels of each channel as the formula Eq. (7):


image


where p(x) and f(x) refers to the distorted and the original image. The parameters a and c regulate contrast and brightness, respectively.


Color Saturation. We perform random linear interpolation between RGB and gray images equivalent to simulate the distortion.


Additive Noise. We use Gaussian noise to simulate any other distortions that are not considered in the attacking layer. We employ a Gaussian noise model (sampling the standard deviation δ ∼ U[0, 0.2]) to simulate imaging noise.

3.4. Loss Function

The proposed method ensures both high stego video quality and precise extraction of secret message. We achieve this by training the modules using the following losses.


image


Table 1. Comparison Results on Extraction Accuracy. “-” means “Without Distortion”. (·) represents Bits Per Frame (BPF).Under different distortion scenarios, our method demonstrates superior performance in comparison.

Table 1. Comparison Results on Extraction Accuracy. “-” means “Without Distortion”. (·) represents Bits Per Frame (BPF).Under different distortion scenarios, our method demonstrates superior performance in comparison.


Attribute Loss. We use the weak feature matching loss [26] to constrain attribute difference before and after embedding secret message. The loss function is defined as follows:


image


where Dj refers to the feature extractor of Discriminator D for the j-th layer, Nj is the number of elements in the j-th layer, and H is the total number of layers. Additionally, h represents the starting layer for computing the weak feature matching loss.


Adversarial Loss. To enhance performance, we use multiscale Discriminator with gradient penalty. We adopt the Hinge version of adversarial loss defined as follows:


image


Secret Loss. To address this, we use the Binary Cross-Entropy loss (BCE) as defined in Eq. (11).


image


Total loss. The total loss is defined as follows:


image


This paper is available on arxiv under CC 4.0 license.


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The Kinetograph's the 1st motion-picture camera. At Kinetograph.Tech, we cover cutting edge tech for video editing.

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