A Survey of Defenses against AI-generated Visual Media: Detection, Disruption, and Authentication
Deep generative models have demonstrated impressive performance in various computer vision applications, including image synthesis, video generation, and medical analysis. Despite their significant advancements, these models may be used for malicious purposes, such as misinformation, deception, and copyright violation. In this paper, we provide a systematic and timely review of research efforts on defenses against AI-generated visual media, covering detection, disruption, and authentication. We review existing methods and summarize the mainstream defense-related tasks within a unified passive and proactive framework. Moreover, we survey the derivative tasks concerning the trustworthiness of defenses, such as their robustness and fairness. For each task, we formulate its general pipeline and propose a taxonomy based on methodological strategies that are uniformly applicable to the primary subtasks. Additionally, we summarize the commonly used evaluation datasets, criteria, and metrics. Finally, by analyzing the reviewed studies, we provide insights into current research challenges and suggest possible directions for future research.
Code (0)
등록된 구현이 없습니다.
Tasks
FairnessImage GenerationMisinformationVideo GenerationSimilar Papers 제목 키워드 기반
As Good As A Coin Toss: Human detection of AI-generated images, videos, audio, and audiovisual stimuli
One of the current principal defenses against weaponized synthetic media continues to be the ability of the targeted individual to visually or auditorily recognize AI-generated content when they encounter it. However, as…
Human DetectionLLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures
As large language models (LLMs) continue to evolve, it is critical to assess the security threats and vulnerabilities that may arise both during their training phase and after models have been deployed. This survey seeks…
SurveyOne Step to the Side: Why Defenses Against Malicious Finetuning Fail Under Adaptive Adversaries
Model providers increasingly release open weights or allow users to fine-tune foundation models through APIs. Although these models are safety-aligned before release, their safeguards can often be removed by fine-tuning …
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks
There is great potential for damage from adversarial learning (AL) attacks on machine-learning based systems. In this paper, we provide a contemporary survey of AL, focused particularly on defenses against attacks on sta…
Anomaly DetectionData PoisoningGeneral ClassificationRobust classificationDefenses Against Multi-Sticker Physical Domain Attacks on Classifiers
Recently, physical domain adversarial attacks have drawn significant attention from the machine learning community. One important attack proposed by Eykholt et al. can fool a classifier by placing black and white sticker…