This survey provides a synthesis of the practical copyright compliance challenges inherent in the input stages of Generative Artificial Intelligence (GenAI) systems, examining the types of copyright-protected data involved, the corresponding challenges for copyright compliance in input data processing practices, and potential mitigation strategies proposed by researchers.
This work reveals, for the first time, that RSMT can substantially amplify privacy leakage of these real training samples, and proposes a lightweight leakage propensity indicator computable from real data alone that reliably identifies high-risk datasets unsuitable for entering RSMT, as a self-assessable mitigation.
Na Li, Boyu Kuang, Hongsheng Hu et al.· arXiv.org· 0 citations
A robust membership inference framework for reliably identifying training data that adopts a mixture-of-experts formulation to jointly model interactions across complementary features derived from multiple MIA methods and AIGT detectors, which can remain robust against ad-versarially generated samples.
Yuanhong Huang, Huili Wang, Xueying Bai et al.· Annual Meeting of the Associ...· 1 citation
The proposed Recursive Self-Correction approach raises model performance from a Political Neutrality Likert scale baseline of 2.14 to 4.56, averaged across all models, demonstrating effective inference-time mitigation of political bias in LLM-generated summaries.
Tejaswi V. Panchagnula, Bruce Coburn, Bryce J. Dietrich et al.· 0 citations
Trained on large corpora of image-text pairs, vision-language models (VLMs) have proven broadly useful across many applications. However, they can still make errors that humans rarely do, particularly when exposed to adversarial inputs crafted to mislead them. Traditional approaches to uncovering such vulnerabilities t...
Thai Huy Nguyen, Khoa Tran, Quan Minh Phan et al.· Annual Conference on Genetic...· 0 citations
The review shows that diffusion and autoregressive foundation models increasingly dominate high-fidelity image, language, and multimodal generation, while GANs, VAEs, and flow-based models remain important in data-limited, structured, scientific, and privacy-aware settings.
A. Javadpour, F. Ja’fari, T. Taleb et al.· IEEE Access· 0 citations
This paper introduces Search in the Model, a novel training-free framework that performs fine-grained identification and rectification of vulnerable neurons directly within the model, and develops a maximum marginal vulnerability criterion to precisely locate the top-K neurons most responsible for model vulnerability.
Yuansan Liu, Ruqing Zhang, Hongru Song et al.· Annual Meeting of the Associ...· 0 citations
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