The Curse of Recursion: Training on Generated Data Makes Models Forget
Fuente:
arXiv
Saved in:
| Main Authors: | Shumailov, Ilia, Shumaylov, Zakhar, Zhao, Yiren, Gal, Yarin, Papernot, Nicolas, Anderson, Ross |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Architectural Neural Backdoors from First Principles
by: Langford, Harry, et al.
Published: (2024)
by: Langford, Harry, et al.
Published: (2024)
When Vision Fails: Text Attacks Against ViT and OCR
by: Boucher, Nicholas, et al.
Published: (2023)
by: Boucher, Nicholas, et al.
Published: (2023)
Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses
by: Glukhov, David, et al.
Published: (2024)
by: Glukhov, David, et al.
Published: (2024)
Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Ahead
by: Schlegel, Viktor, et al.
Published: (2025)
by: Schlegel, Viktor, et al.
Published: (2025)
Reasoning Introduces New Poisoning Attacks Yet Makes Them More Complicated
by: Foerster, Hanna, et al.
Published: (2025)
by: Foerster, Hanna, et al.
Published: (2025)
Watermarking Needs Input Repetition Masking
by: Khachaturov, David, et al.
Published: (2025)
by: Khachaturov, David, et al.
Published: (2025)
UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI
by: Shumailov, Ilia, et al.
Published: (2024)
by: Shumailov, Ilia, et al.
Published: (2024)
ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks
by: Clifford, Eleanor, et al.
Published: (2022)
by: Clifford, Eleanor, et al.
Published: (2022)
Iteratively Prompting Multimodal LLMs to Reproduce Natural and AI-Generated Images
by: Naseh, Ali, et al.
Published: (2024)
by: Naseh, Ali, et al.
Published: (2024)
BSPA: Exploring Black-box Stealthy Prompt Attacks against Image Generators
by: Tian, Yu, et al.
Published: (2024)
by: Tian, Yu, et al.
Published: (2024)
IAG: Input-aware Backdoor Attack on VLM-based Visual Grounding
by: Li, Junxian, et al.
Published: (2025)
by: Li, Junxian, et al.
Published: (2025)
Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning
by: Rinberg, Roy, et al.
Published: (2025)
by: Rinberg, Roy, et al.
Published: (2025)
Stealing User Prompts from Mixture of Experts
by: Yona, Itay, et al.
Published: (2024)
by: Yona, Itay, et al.
Published: (2024)
Beyond Labeling Oracles: What does it mean to steal ML models?
by: Shafran, Avital, et al.
Published: (2023)
by: Shafran, Avital, et al.
Published: (2023)
VLMs Can Aggregate Scattered Training Patches
by: Zhou, Zhanhui, et al.
Published: (2025)
by: Zhou, Zhanhui, et al.
Published: (2025)
LLM Dataset Inference: Did you train on my dataset?
by: Maini, Pratyush, et al.
Published: (2024)
by: Maini, Pratyush, et al.
Published: (2024)
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning
by: Zhao, Joshua C., et al.
Published: (2024)
by: Zhao, Joshua C., et al.
Published: (2024)
Are GUI Agents Focused Enough? Automated Distraction via Semantic-level UI Element Injection
by: Yang, Wenkui, et al.
Published: (2026)
by: Yang, Wenkui, et al.
Published: (2026)
Image-Based Geolocation Using Large Vision-Language Models
by: Liu, Yi, et al.
Published: (2024)
by: Liu, Yi, et al.
Published: (2024)
MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance
by: Pi, Renjie, et al.
Published: (2024)
by: Pi, Renjie, et al.
Published: (2024)
Privacy-Preserving Federated Learning with Verifiable Fairness Guarantees
by: Ali, Mohammed Himayath, et al.
Published: (2026)
by: Ali, Mohammed Himayath, et al.
Published: (2026)
Visual Contextual Attack: Jailbreaking MLLMs with Image-Driven Context Injection
by: Miao, Ziqi, et al.
Published: (2025)
by: Miao, Ziqi, et al.
Published: (2025)
SlowBA: An efficiency backdoor attack towards VLM-based GUI agents
by: Li, Junxian, et al.
Published: (2026)
by: Li, Junxian, et al.
Published: (2026)
Rethinking Bottlenecks in Safety Fine-Tuning of Vision Language Models
by: Ding, Yi, et al.
Published: (2025)
by: Ding, Yi, et al.
Published: (2025)
Contextual Image Attack: How Visual Context Exposes Multimodal Safety Vulnerabilities
by: Xiong, Yuan, et al.
Published: (2025)
by: Xiong, Yuan, et al.
Published: (2025)
Effective and Efficient Adversarial Detection for Vision-Language Models via A Single Vector
by: Huang, Youcheng, et al.
Published: (2024)
by: Huang, Youcheng, et al.
Published: (2024)
Are Vision-Language Models Safe in the Wild? A Meme-Based Benchmark Study
by: Lee, DongGeon, et al.
Published: (2025)
by: Lee, DongGeon, et al.
Published: (2025)
Self-adaptive Dataset Construction for Real-World Multimodal Safety Scenarios
by: Qu, Jingen, et al.
Published: (2025)
by: Qu, Jingen, et al.
Published: (2025)
Doubly-Universal Adversarial Perturbations: Deceiving Vision-Language Models Across Both Images and Text with a Single Perturbation
by: Kim, Hee-Seon, et al.
Published: (2024)
by: Kim, Hee-Seon, et al.
Published: (2024)
Evolving Contextual Safety in Multi-Modal Large Language Models via Inference-Time Self-Reflective Memory
by: Zhang, Ce, et al.
Published: (2026)
by: Zhang, Ce, et al.
Published: (2026)
Forget-MI: Machine Unlearning for Forgetting Multimodal Information in Healthcare Settings
by: Hardan, Shahad, et al.
Published: (2025)
by: Hardan, Shahad, et al.
Published: (2025)
Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy
by: Hayes, Jamie, et al.
Published: (2024)
by: Hayes, Jamie, et al.
Published: (2024)
Interpreting the Repeated Token Phenomenon in Large Language Models
by: Yona, Itay, et al.
Published: (2025)
by: Yona, Itay, et al.
Published: (2025)
Rethinking Machine Unlearning in Image Generation Models
by: Liu, Renyang, et al.
Published: (2025)
by: Liu, Renyang, et al.
Published: (2025)
Recovering the Pre-Fine-Tuning Weights of Generative Models
by: Horwitz, Eliahu, et al.
Published: (2024)
by: Horwitz, Eliahu, et al.
Published: (2024)
Reconstructing Training Data From Real World Models Trained with Transfer Learning
by: Oz, Yakir, et al.
Published: (2024)
by: Oz, Yakir, et al.
Published: (2024)
SafeGen: Mitigating Sexually Explicit Content Generation in Text-to-Image Models
by: Li, Xinfeng, et al.
Published: (2024)
by: Li, Xinfeng, et al.
Published: (2024)
One Pic is All it Takes: Poisoning Visual Document Retrieval Augmented Generation with a Single Image
by: Shereen, Ezzeldin, et al.
Published: (2025)
by: Shereen, Ezzeldin, et al.
Published: (2025)
When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?
by: Wang, Shang, et al.
Published: (2024)
by: Wang, Shang, et al.
Published: (2024)
Color Matters: Demosaicing-Guided Color Correlation Training for Generalizable AI-Generated Image Detection
by: Zhong, Nan, et al.
Published: (2026)
by: Zhong, Nan, et al.
Published: (2026)
Similar Items
-
Architectural Neural Backdoors from First Principles
by: Langford, Harry, et al.
Published: (2024) -
When Vision Fails: Text Attacks Against ViT and OCR
by: Boucher, Nicholas, et al.
Published: (2023) -
Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses
by: Glukhov, David, et al.
Published: (2024) -
Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Ahead
by: Schlegel, Viktor, et al.
Published: (2025) -
Reasoning Introduces New Poisoning Attacks Yet Makes Them More Complicated
by: Foerster, Hanna, et al.
Published: (2025)