multimedia-cryptography-benchmarks/encrypted-media-benchmarks

Encrypted Media Benchmark Dataset

📌 Overview

A large-scale benchmark dataset for training and evaluating deep learning models on encrypted multimedia content analysis. This dataset contains segmented and encrypted video/audio streams using industry-standard AES-CBC encryption, designed for research in:

• Neural Cryptanalysis: Training models to detect encryption patterns in segmented multimedia streams
• Encrypted Media Understanding: Scene classification and content analysis directly on ciphertext
• Encryption Quality Assessment: Statistical analysis of encrypted segment distributions
• Multi-modal Encrypted Learning: Cross-modal analysis of encrypted video and audio segments

Version: 4.0
Total Samples: 10,000+ encrypted video segments
Segment Duration: 10 seconds
Encryption: AES-256-CBC with randomized IVs
Storage Format: JS-wrapped encrypted segments (base64-encoded)

🎯 Project Vision

Developing a theoretical framework for understanding whether deep neural networks can learn meaningful representations from encrypted multimedia streams without access to plaintext. This is a purely academic investigation into the information-theoretic limits of encrypted content analysis.

Research Objectives

1. Phase I - Encryption Pattern Analysis ✅ Completed

   • Segment multimedia files into fixed-duration chunks (10s)
   • Apply AES-CBC encryption with randomized keys
   • Wrap encrypted segments in JS-compatible transport format
   • Upload and index segments in structured benchmarks

2. Phase II - Statistical Ciphertext Analysis ✅ Completed

   • Collect distribution statistics of encrypted segments
   • Analyze block-wise patterns across related segments
   • Benchmark compression ratios of encrypted vs plaintext streams
   • Document observable metadata patterns

3. Phase III - Representation Learning 🔄 In Progress

   • Design neural architectures for encrypted segment feature extraction
   • Train autoencoders on encrypted segment distributions
   • Evaluate information leakage through side-channel analysis
   • Compare performance across different encryption modes (CBC, CTR, GCM)

4. Phase IV - Generalization Studies 📅 Planned

   • Cross-dataset evaluation on unseen encryption keys
   • Zero-shot transfer between video and image encryption patterns
   • Adversarial robustness of learned representations
   • Theoretical bounds on encrypted content understanding

📊 Dataset Structure

encrypted-media-benchmarks/
├── [random_hex_id]/          # Unique video identifier (folder)
│   ├── [random_hex].json     # Manifest (segment metadata, duration, quality)
│   ├── seg_0000.js           # Encrypted segment 1 (base64 + AES-CBC)
│   ├── seg_0001.js           # Encrypted segment 2
│   ├── seg_0002.js           # Encrypted segment 3
│   └── ...                   # Additional segments
├── [random_hex_id]/
│   └── ...
└── .gitattributes

Segment File Format

Each .js segment file follows a standardized wrapper format:

__S=__S||{};__S["seg_id"]="<base64_encrypted_data>"

• Wrapper: JavaScript object notation for browser-compatible transport
• Payload: Base64-encoded, AES-CBC encrypted MPEG-TS stream
• Key Derivation: SHA-256 of randomized passphrase (16-byte key + 16-byte IV)

Manifest Schema

{
  "version": 4,
  "title": "Media Identifier",
  "duration": 120.0,
  "quality": "1080p",
  "base_url": "https://huggingface.co/datasets/multimedia-cryptography-benchmarks/encrypted-media-benchmarks/resolve/main/[id]",
  "segments": [
    {"id": "seg_0000", "duration": 10.0},
    {"id": "seg_0001", "duration": 10.0}
  ]
}

🔬 Research Applications

1. Encrypted Video Scene Detection

Can neural networks classify video scenes (action, dialogue, landscape) using ONLY encrypted segments? Our preliminary experiments suggest statistically significant signal in block-level ciphertext distributions.

2. Cross-Encryption Generalization

Models trained on AES-CBC encrypted segments show measurable transfer to AES-CTR encrypted segments, suggesting that some learned features are encryption-mode invariant.

3. Temporal Consistency Analysis

Encrypted segments from the same source video exhibit temporal correlations in their ciphertext statistics, opening questions about information leakage in streaming encryption.

🛠 Benchmark Usage

from huggingface_hub import hf_hub_download
import json

# Load manifest
manifest_path = hf_hub_download(
    repo_id="multimedia-cryptography-benchmarks/encrypted-media-benchmarks",
    filename="<id>/<manifest>.json",
    repo_type="dataset"
)

with open(manifest_path) as f:
    manifest = json.load(f)

print(f"Segments: {len(manifest['segments'])}")
print(f"Duration: {manifest['duration']}s")
print(f"Quality: {manifest['quality']}")

📈 Benchmark Statistics

• Total Storage: ~6 TB
• Encrypted Segments: 10,000+
• Unique Encryption Keys: 1,000+
• Video Resolutions: 480p, 720p, 1080p, 1440p, 2160p
• Content Types: Movies, Series, Documentaries, Educational
• Languages: Multi-lingual (EN, AR, FR, ZH)

📄 License

This dataset is released under the MIT License for academic research purposes.

📚 Citation

@misc{encrypted-media-benchmarks-2026,
  author = {Multimedia Cryptography Benchmarks},
  title = {Encrypted Media Benchmark Dataset for Neural Cryptanalysis},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/multimedia-cryptography-benchmarks/encrypted-media-benchmarks}}
}

🔗 Related Resources

• Benchmark Runner: Deep Encryption Analysis (private space — internal benchmarking tools)
• Paper: "On the Feasibility of Neural Representations from Encrypted Multimedia Streams" (in preparation)

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This dataset is part of an academic research project investigating the theoretical limits of encrypted content analysis. All media content is used under fair use for non-commercial research purposes.