Scientists Find Placement and Precision Matter in Hiding Secret Messages in Videos

:::info Authors:

(1) Xueying Mao, School of Computer Science, Fudan University, China (xymao22@[email protected]);

(2) Xiaoxiao Hu, School of Computer Science, Fudan University, China ([email protected]);

(3) Wanli Peng, School of Computer Science, Fudan University, China ([email protected]);

(4) Zhenliang Gan, School of Computer Science, Fudan University, China (zlgan23@[email protected]);

(5) Qichao Ying, School of Computer Science, Fudan University, China ([email protected]);

(6) Zhenxing Qian, School of Computer Science, Fudan University, China and a Corresponding Author ([email protected]);

(7) Sheng Li, School of Computer Science, Fudan University, China ([email protected]);

(8) Xinpeng Zhang, School of Computer Science, Fudan University, China ([email protected]).

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:::tip Editor's note: This is Part 6 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.

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• Abstract and 1. Introduction
• 2. Related Work
• 3. Proposed Approach
• 4. Experiments
• 4.1. Experimental Setups
• 4.2. Performance Analysis
• 4.3. Ablation Study
• Conclusions and References

Embedding Position of Secret Message. In our generation network with 9 Secret-ID blocks, we explore different positions for embedding the secret message. We divide the secret message into two 9-bit segments and allocate their positions. In detail, Setting (a): 1st-4th blocks and 5th-9th blocks.

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\ Setting (b): 1st-2nd blocks and 3rd-4th blocks. Setting (c): 5th-6th blocks and 7th-8th blocks. They are in comparison of the standard setting of RoGVS: 1st-3rd blocks and 4th-6th blocks.

\ Table 2 displays the performance for these four setups. Both Settings b and c show a considerable decrease compared to Settings a and d, suggesting that adding more Secret-ID blocks improves performance. Notably, Setting c outperforms Setting b, indicating the higher influence of subsequent blocks on the generated image.

\ Ablation on Attacking Layer, λ & Discriminator. Fig 5 shows even without the module, our method demonstrates considerable robustness, surpassing the three comparative methods. The addition of attacking layer improves accuracy by an average of 6%. Table 3 presents the impact of λ on the extraction accuracy. More ablation results on λ and the discriminator are displayed in the supplement.

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:::info This paper is available on arxiv under CC 4.0 license.

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