Generative AI and Digital Ecosystem Resilience: A Proactive Lifecycle-Based Survey

• URL: http://arxiv.org/abs/2606.00136v1
• Date: Thu, 28 May 2026 22:22:56 GMT
• Title: Generative AI and Digital Ecosystem Resilience: A Proactive Lifecycle-Based Survey
• Abstract summary: The proliferation of adversarial synthetic content, accelerated by Generative AI (GenAI) is rendering traditional reactive detection methods ineffective.<n>This survey synthesizes emerging research to demonstrate a paradigm shift toward the proactive detection of emerging inauthentic narratives.
• Score: 0.410492188035848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of adversarial synthetic content, accelerated by Generative AI (GenAI) is rendering traditional reactive detection methods ineffective. This survey synthesizes emerging research to demonstrate a paradigm shift toward the proactive detection of emerging inauthentic narratives. In this survey, we adopt a unified, lifecycle-based taxonomy to combine socio-technical lifecycle models of adversarial campaigns with advanced computational methodologies for emerging inauthentic narrative detection. By structuring the analysis around the C5 Interaction Model (Context, Causes, Content, Cycle of Amplification, Consequences), we integrate different research streams from machine learning and social science. To differentiate spread patterns of synthetic amplification from authentic baseline traffic, this paper surveys state-of-the-art techniques for modeling the creation, seeding, and propagation of fresh narratives, including the analysis of Coordinated Inauthentic Behavior (CIB), epidemiological modeling, and Hawkes process. This survey also provides a systematic review of proactive detection methods for adversarial threats at different stages in the C5 interaction model, specifically, anomaly detection in high-dimensional embedding spaces, unsupervised coordination detection on multi-layer graphs, and agentic AI systems. Finally, this survey addresses challenges posed by GenAI, including the difficulty of tracking rapidly changing threats and multi-level distributional drift, and it outlines a future research agenda focused on detecting anomalous clusters and building anticipatory and resilient systems. This survey provides a comprehensive, lifecycle-based review of methods for the proactive detection of emerging synthetic threats for more resilient information ecosystems.

Related papers

• The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance [47.03825808787752]
  This article presents findings from the first wave of a longitudinal expert perception survey (N=21) involving AI researchers, policymakers, and disinformation specialists.<n>It examines the perceived severity of multimodal threats -- text, image, audio, and video -- and evaluates current mitigation strategies.<n>Results indicate that while deepfake video presents immediate "shock" value, large-scale text generation poses a systemic risk of "epistemic fragmentation"
  arXiv  Detail & Related papers  (2026-02-02T13:45:12Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Improving Deepfake Detection with Reinforcement Learning-Based Adaptive Data Augmentation [60.04281435591454]
  CRDA (Curriculum Reinforcement-Learning Data Augmentation) is a novel framework guiding detectors to progressively master multi-domain forgery features.<n>Central to our approach is integrating reinforcement learning and causal inference.<n>Our method significantly improves detector generalizability, outperforming SOTA methods across multiple cross-domain datasets.
  arXiv  Detail & Related papers  (2025-11-10T12:45:52Z)
• Anomaly Detection and Generation with Diffusion Models: A Survey [51.61574868316922]
  Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing.<n>Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest.<n>This survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.
  arXiv  Detail & Related papers  (2025-06-11T03:29:18Z)
• Methods and Trends in Detecting AI-Generated Images: A Comprehensive Review [0.17188280334580194]
  Generative Adversarial Networks (GANs), Diffusion Models, and Variational Autoencoders (VAEs) have enabled the synthesis of high-quality multimedia data.<n>These advancements have also raised significant concerns regarding adversarial attacks, unethical usage, and societal harm.<n>This survey provides a comprehensive review of state-of-the-art techniques for detecting and classifying synthetic images generated by advanced generative AI models.
  arXiv  Detail & Related papers  (2025-02-21T03:16:18Z)
• The Cat and Mouse Game: The Ongoing Arms Race Between Diffusion Models and Detection Methods [0.0]
  Diffusion models have transformed synthetic media generation, offering unmatched realism and control over content creation. They can facilitate deepfakes, misinformation, and unauthorized reproduction of copyrighted material. In response, the need for effective detection mechanisms has become increasingly urgent.
  arXiv  Detail & Related papers  (2024-10-24T15:51:04Z)
• Adjoint Sensitivity Analysis on Multi-Scale Bioprocess Stochastic Reaction Network [2.6130735302655554]
  We introduce an adjoint sensitivity approach to expedite the learning of mechanistic model parameters. In this paper, we consider enzymatic analysis (SA) representing a multi-scale bioprocess mechanistic model.
  arXiv  Detail & Related papers  (2024-05-07T05:06:45Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.