Researchers Test Secret Message Embedding in Videos Using AI

Table of Links

• 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

4. EXPERIMENTS

4.1. Experimental Setups

Datasets. We use Vggface2 [61] for training and FFHQ [15] for validation. We crop and resize facial areas to a fixed 224 × 224 resolution for input images. To analyze quality and performance, we randomly select 100 videos from DeepFake MNIST+ [65] to evaluate the performance.

Fig. 3. Qualitative Analysis of Stego Videos. Original represents frames within the cover videos.

Evaluation Metrics. We employ Bits Per Frame (BPF), quantifying the bits number of secret message per frame in the stego video. To assess robustness, we evaluate secret message extraction accuracy under various scenarios. For security assessment, we use three steganalysis methods [62, 63, 64] to demonstrate our method’s anti-detection capability.

Baselines. To ensure fair comparison in our experiments, we align HiDDeN and LSB to this capacity. Detailed methods of HiDDeN and LSB are available in the supplementary materials. Additionally, due to its PU-based design, PWRN has a limited capacity of 15 BPF when resizing input images to 224 × 224.