mlsanityData to 7 Oct 2026

DeepBlur: A Simple and Effective Method for Natural Image Obfuscation

arXiv 2104.02655 ↗First posted 31 Mar 2021cs.CV cs.CR cs.MMcited by 13

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Abstract (arXiv)

There is a growing privacy concern due to the popularity of social media and surveillance systems, along with advances in face recognition software. However, established image obfuscation techniques are either vulnerable to re-identification attacks by human or deep learning models, insufficient in preserving image fidelity, or too computationally intensive to be practical. To tackle these issues, we present DeepBlur, a simple yet effective method for image obfuscation by blurring in the latent space of an unconditionally pre-trained generative model that is able to synthesize photo-realistic facial images. We compare it with existing methods by efficiency and image quality, and evaluate against both state-of-the-art deep learning models and industrial products (e.g., Face++, Microsoft face service). Experiments show that our method produces high quality outputs and is the strongest defense for most test cases.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 37 of its 59 references that are in the map. First posted on arXiv 31 Mar 2021.

Builds on

37 of 59 references.

References · 37

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25 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 1; 2023: 2; 2024: 4; 2025: 2; 2026: 1.

13 papers.

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  1. 2023CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Searchcited by 42
  2. 2021SoK: Anti-Facial Recognition Technologycited by 16
  3. 2024Face De-identification: State-of-the-art Methods and Comparative Studiescited by 9
  4. 2021On Generating Identifiable Virtual Facescited by 8
  5. 2021Addressing Privacy Threats from Machine Learningcited by 1
  6. 2022Synthetic Dataset Generation for Privacy-Preserving Machine Learningcited by 1
  7. 2024A Key-Driven Framework for Identity-Preserving Face Anonymizationcited by 1
  8. 2025FaceAnonyMixer: Cancelable Faces via Identity Consistent Latent Space Mixingcited by 1
  9. 2023Privacy-Preserving Medical Image Classification through Deep Learning and Matrix Decompositioncited by 0
  10. 2024Gaussian Differentially Private Human Faces Under a Face Radial Curve Representationcited by 0
  11. 2024Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy Versus Performancecited by 0
  12. 2025On the Importance of Conditioning for Privacy-Preserving Data Augmentationcited by 0
1 more
  1. 2026Bit-ViP: Leveraging Bit-planes to Preserve Visual Privacy in Images through Obfuscationcited by 0