GHOSTDROP // DOCUMENTATION

GHOSTDROP is a volatile, peer-to-peer messaging framework designed for absolute privacy and zero digital footprint.

Your data never touches a server. Connections are established directly browser-to-browser via WebRTC.

All traffic is protected by hardware-accelerated DTLS/SCTP encryption. Maximum security with zero latency.

You can choose the duration of a message through the "SEC." input field, further the chat can be terminated automatically after 120s of no activity or through the "NUKE" button.

Refreshing or closing the tab wipes the entire session instantly from RAM.

Automated background noise generator masks communication patterns from network sniffing.

Hit 'ESC' to hide the chat behind an academic research paper on GAN Ablation Studies.

Our experimental framework utilizes a standardized architecture to test the impact of generator mapping. We strictly adhered to a learning rate ($\alpha$) of 0.0002, which our preliminary data suggests is the "sweet spot" for maintaining equilibrium between the Discriminator ($D$) and Generator ($G$). The latent dimension ($z$) was fixed at 50 to ensure a sufficiently complex manifold without inducing training divergence.

The 32-map model demonstrated superior convergence compared to higher-capacity variants. Below is the summarized performance data across the 50-epoch training cycle:

Figure 1 (illustrated below) represents the Loss Curve ($L_G$ vs $L_D$). Notice the stabilization post-epoch 35 where the 32-map capacity prevents the vanishing gradient problem.

The training was optimized for product-specific categorical identifiers, specifically monitoring mcatid1 and subcatid1 for semantic consistency during image generation. Future iterations will explore the Transition_Type variable to further refine the generator's ability to handle city-specific data (cityid1).

[1] Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
[2] Rahman, N. (2025). Internal Ablation Study on Latent Dimensions and Network Capacity.