Copyleft for Alleviating AIGC Copyright Dilemma: What-if Analysis, Public Perception and Implications

As AIGC has impacted our society profoundly in the past years, ethical issues have received tremendous attention. The most urgent one is the AIGC copyright dilemma, which can immensely stifle the development of AIGC and greatly cost the entire society. Given the complexity of AIGC copyright governance and the fact that no perfect solution currently exists, previous work advocated copyleft on AI governance but without substantive analysis. In this paper, we take a step further to explore the feasibility of copyleft to alleviate the AIGC copyright dilemma. We conduct a mixed-methods study from two aspects: qualitatively, we use a formal what-if analysis to clarify the dilemma and provide case studies to show the feasibility of copyleft; quantitatively, we perform a carefully designed survey to find out how the public feels about copylefting AIGC. The key findings include: a) people generally perceive the dilemma, b) they prefer to use authorized AIGC under loose restriction, and c) they are positive to copyleft in AIGC and willing to use it in the future.

Towards Function Space Mesh Watermarking: Protecting the Copyright of Signed Distance Fields

The signed distance field (SDF) represents 3D geometries in continuous function space. Due to its continuous nature, explicit 3D models (e.g., meshes) can be extracted from it at arbitrary resolution, which means losing the SDF is equivalent to losing the mesh. Recent research has shown meshes can also be extracted from SDF-enhanced neural radiance fields (NeRF). Such a signal raises an alarm that any implicit neural representation with SDF enhancement can extract the original mesh, which indicates identifying the SDF's intellectual property becomes an urgent issue. This paper proposes FuncMark, a robust and invisible watermarking method to protect the copyright of signed distance fields by leveraging analytic on-surface deformations to embed binary watermark messages. Such deformation can survive isosurfacing and thus be inherited by the extracted meshes for further watermark message decoding. Our method can recover the message with high-resolution meshes extracted from SDFs and detect the watermark even when mesh vertices are extremely sparse. Furthermore, our method is robust even when various distortions (including remeshing) are encountered. Extensive experiments demonstrate that our \tool significantly outperforms state-of-the-art approaches and the message is still detectable even when only 50 vertex samples are given.

EFFL: Egalitarian Fairness in Federated Learning for Mitigating Matthew Effect

Recent advances in federated learning (FL) enable collaborative training of machine learning (ML) models from large-scale and widely dispersed clients while protecting their privacy. However, when different clients' datasets are heterogeneous, traditional FL mechanisms produce a global model that does not adequately represent the poorer clients with limited data resources, resulting in lower accuracy and higher bias on their local data. According to the Matthew effect, which describes how the advantaged gain more advantage and the disadvantaged lose more over time, deploying such a global model in client applications may worsen the resource disparity among the clients and harm the principles of social welfare and fairness. To mitigate the Matthew effect, we propose Egalitarian Fairness Federated Learning (EFFL), where egalitarian fairness refers to the global model learned from FL has: (1) equal accuracy among clients; (2) equal decision bias among clients. Besides achieving egalitarian fairness among the clients, EFFL also aims for performance optimality, minimizing the empirical risk loss and the bias for each client; both are essential for any ML model training, whether centralized or decentralized. We formulate EFFL as a constrained multi-constrained multi-objectives optimization (MCMOO) problem, with the decision bias and egalitarian fairness as constraints and the minimization of the empirical risk losses on all clients as multiple objectives to be optimized. We propose a gradient-based three-stage algorithm to obtain the Pareto optimal solutions within the constraint space. Extensive experiments demonstrate that EFFL outperforms other state-of-the-art FL algorithms in achieving a high-performance global model with enhanced egalitarian fairness among all clients.

Deep Boosting Robustness of DNN-based Image Watermarking via DBMark

In this paper, we present DBMark, a new end-to-end digital image watermarking framework to deep boost the robustness of DNN-based image watermarking. The key novelty is the synergy of the Invertible Neural Networks(INNs) and effective watermark features generation. The framework generates watermark features with redundancy and error correction ability through message processing, synergized with the powerful information embedding and extraction capabilities of Invertible Neural Networks to achieve higher robustness and invisibility. Extensive experiment results demonstrate the superiority of the proposed framework compared with the state-of-the-art ones under various distortions.