CausalVE: Face Video Privacy Encryption via Causal Video Prediction
Yubo Huang, Wenhao Feng, Xin Lai, Zixi Wang, Jingzehua Xu, Shuai, Zhang, Hongjie He, Fan Chen
TL;DR
CausalVE is a neural network framework that enhances face video privacy by using causal video prediction and reversible neural networks to securely hide and transmit secret videos, outperforming existing methods.
Contribution
The paper introduces CausalVE, a novel neural network approach combining diffusion models and reversible neural networks for secure face video privacy and secret data dissemination.
Findings
CausalVE achieves superior security in public video sharing.
It outperforms state-of-the-art methods qualitatively and quantitatively.
The method effectively hides secret videos with good visual quality.
Abstract
Advanced facial recognition technologies and recommender systems with inadequate privacy technologies and policies for facial interactions increase concerns about bioprivacy violations. With the proliferation of video and live-streaming websites, public-face video distribution and interactions pose greater privacy risks. Existing techniques typically address the risk of sensitive biometric information leakage through various privacy enhancement methods but pose a higher security risk by corrupting the information to be conveyed by the interaction data, or by leaving certain biometric features intact that allow an attacker to infer sensitive biometric information from them. To address these shortcomings, in this paper, we propose a neural network framework, CausalVE. We obtain cover images by adopting a diffusion model to achieve face swapping with face guidance and use the speech…
Peer Reviews
Decision·ICLR 2025 Conference Withdrawn Submission
1. CausalVE combines causal reasoning, reversible neural networks, and hybrid diffusion models to achieve high-fidelity face swapping and robust privacy preservation. 2. The framework offers privacy protection without compromising video quality, enabling natural and realistic facial video transformations. 3. By embedding the original video within the cover video, CausalVE maintains a balance between privacy and the potential for legitimate retrieval, thanks to its reversible neural network.
1. The paper could mislead readers into believing that the entire video frame is processed and hidden rather than just the facial region. Since the facial region occupies only part of a frame, the data requirements for concealing and generating only the face differ substantially from handling the entire frame. Clarifying this distinction early on would improve readability and prevent misunderstandings. 2. Due to the potential for confusion about the processing scope, it’s unclear whether the pa
- The protection of privacy is a genuine concern and efforts towards that are highly needed. - Although not novel, still the use of a cover image through face guidance is interesting.
- One of the primary weaknesses of the paper is its editorial limitation. The paper is hard to read and follow. For example, a significant amount of information is missing or not adequately presented. For instance, in line 094, what physical information has been used? What is the role of a pseudo-video (line 100)? At line 100, what form of frequency is used to divide frames? - The motivation for using the diffusion model is not clear. The field of steganography is not new and several research w
- The CausalVE framework uses causal reasoning to guide the video prediction process, producing cover videos that are both visually convincing and capable of securely carrying hidden information. - This framework leverages a reversible neural network, allowing the original video to be concealed within a pseudo-video and accurately recovered using a key, thereby safeguarding personal data while enabling secure public distribution. - CausalVE incorporates a hybrid diffusion model that uses ident
**Some major comments:** - The manuscript lacks some visual results display and qualitative evaluation. - The framework proposed in this manuscript integrates multiple tasks, resulting in incomplete introduction of each task. **Some other minor comments:** - The author's summary of innovation is too confusing. I need to spend time reading the full text to understand it. The author still needs to strengthen his writing of the manuscript. - The drawings in this manuscript are too rough, and t
1. A new understandable framework to protect face privacy in video. 2. The structure of the writing is clear
1. Motivation of the Work is Ambiguous: The rationale behind the need for reversibility is unclear. Specifically, I do not see the significance of restoring the original video. The primary purpose of privacy protection is to eliminate sensitive information. In particular, "irreversibility" would more effectively enhance the strength of privacy protection. The authors need to clarify the scenarios in which reversibility is applicable. 2. Implementation Approach is Unreasonable: The authors propo
- Protecting the original video using video steganography techniques. - Using symmetric encryption to encode and decode the original video ensures the preservation of information during transmission.
- Application. The proposed method is positioned as a way to protect user privacy on public platforms. However, this presents an inherent contradiction: if users genuinely wish to protect their privacy, they wouldn’t need to upload videos to a public platform. The only scenario where this might make sense is if users intentionally want to share hidden information through public platforms, which raises potential societal concerns. - Security. While the use of a reversible neural network ensures
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Taxonomy
TopicsBiometric Identification and Security · Advanced Steganography and Watermarking Techniques · Face recognition and analysis
MethodsDiffusion