antideepfake

v0.0.1 suspicious
4.0
Medium Risk

Anti-deepfake detection toolkit by Oravys Inc.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal risks in terms of network usage, shell execution, and code obfuscation. However, the metadata suggests it was recently created with limited maintainer history, which raises some concerns.

  • Metadata risk due to recent creation and lack of maintainer history
  • Low risk in network, shell, and obfuscation categories
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external services.
  • Shell: No shell execution detected, indicating the package likely does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of being newly created with limited maintainer history and an incomplete author profile, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (1.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: oravys.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with antideepfake 
Create a Python-based desktop application named 'DeepSentry' that utilizes the 'antideepfake' package to detect deepfake videos. DeepSentry should be designed for both novice and advanced users, offering a user-friendly interface alongside powerful detection capabilities. Here are the key steps and features of the project:

1. **Setup Environment**: Install Python, necessary libraries, and the 'antideepfake' package.
2. **User Interface**: Develop a simple yet intuitive GUI using Tkinter or PyQt for file uploads and video playback.
3. **Video Upload & Preprocessing**: Allow users to upload video files directly from their computer. Implement preprocessing steps such as resizing and normalization if required by the 'antideepfake' package.
4. **Detection Engine**: Integrate the 'antideepfake' package to analyze uploaded videos. Use its core functionalities to detect deepfake content within the videos.
5. **Results Presentation**: Display the detection results in real-time or after processing. Highlight suspicious areas or frames where deepfake activity is detected.
6. **Additional Features**:
   - Include a feature to save the analysis report in a readable format like PDF or CSV.
   - Provide an option for batch processing multiple video files at once.
   - Offer adjustable sensitivity settings for more tailored detection experiences.
7. **Documentation & Testing**: Write comprehensive documentation explaining how to use DeepSentry and how it integrates 'antideepfake'. Conduct thorough testing to ensure accuracy and reliability of the detection process.

This project aims to provide a practical tool for individuals and organizations to combat the growing threat of deepfakes, leveraging the robust detection capabilities of 'antideepfake'.

💬 Discussion Feed

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