antideepfake-eu

v0.0.1 suspicious
4.0
Medium Risk

Anti-deepfake EU compliance edition by Oravys Inc.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network, shell, and obfuscation activities but has a high metadata risk due to missing maintainer history and author name, suggesting potential malicious intent.

  • High metadata risk due to missing maintainer history and author name
  • Low risk in network, shell, and obfuscation activities
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package is suspicious due to its lack of maintainer history and a missing author name, indicating potential malicious intent.

📦 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-eu 
Develop a web-based mini-application named 'DeepGuard' that helps users detect deepfake videos and ensure they comply with the European Union's anti-deepfake regulations. The application should allow users to upload video files, analyze them for deepfake content using the 'antideepfake-eu' package, and provide a report on whether the video complies with EU standards. Here are the steps and features you should include:

1. **User Interface Design**: Create an intuitive user interface where users can easily upload video files. Include options for file selection, video preview, and a start analysis button.
2. **Video Upload Handling**: Implement backend functionality to securely handle video uploads. Ensure videos are stored temporarily and deleted after processing to maintain user privacy.
3. **Deepfake Detection**: Utilize the 'antideepfake-eu' package to analyze uploaded videos for deepfake content. This package provides tools to identify manipulated images and videos, ensuring compliance with EU regulations.
4. **Compliance Report Generation**: After analyzing the video, generate a detailed report indicating whether the video complies with EU anti-deepfake laws. The report should include a summary of findings, any detected manipulations, and recommendations for further action if necessary.
5. **Feedback Mechanism**: Allow users to view the results of the analysis and download the compliance report. Provide feedback options where users can suggest improvements or report issues with the analysis.
6. **Security and Privacy Measures**: Ensure all data is handled securely. Use encryption for data in transit and at rest, and implement strict access controls.
7. **Documentation and User Guides**: Provide comprehensive documentation for both end-users and developers. Explain how to use the application, interpret the reports, and integrate 'antideepfake-eu' into other systems.
8. **Testing and Validation**: Conduct thorough testing to ensure the application works as expected across different devices and browsers. Validate the accuracy of the deepfake detection through various test cases and real-world scenarios.

By following these guidelines, you'll create a powerful tool that not only detects deepfakes but also ensures compliance with EU regulations, promoting trust and transparency online.

💬 Discussion Feed

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