Scalable Detection of Adversarial Synthetic Slop and Coordinated Media Abuse: A LoRA-Enabled Multimodal Defense System

Skip to main content

Google

Research

Search

Scalable Detection of Adversarial Synthetic Slop and Coordinated Media Abuse: A LoRA-Enabled Multimodal Defense System

Abhinav Mathur

Birant Orten

Claire Liu

Kelvin Tan

Yifei Liu

2026

Download

Google Scholar

Copy Bibtex

Abstract

Online video platforms face an exponential challenge in detecting and mitigating the flood of AI-generated "slop" and synthetic spam perpetuated by coordinated malicious actors. This content is increasingly designed to exploit the limitations of traditional media forensics, often utilizing generative AI to produce unique, localized variations of harmful or low-quality material at scale. Traditional content-centric moderation fails against this coordinated, adversarial generation strategy.<br>This paper presents a novel, scalable defense system deployed at a major Online Video Platform (OVP) to identify and terminate clusters of coordinated accounts exhibiting a prevalence of adversarial synthetic content. The approach leverages a multi-faceted architecture incorporating two core machine learning components: a robust Coordinated Bot-Net Detector (via Account Relatedness) and a Synthetic Pattern Classifier (formerly BT Classifier). Crucially, we introduce an advanced AI enhancement layer utilizing Large Language Models (LLMs), specialized via Low-Rank Adaptation (LoRA) and Automatic Prompt Optimization (APO), to achieve rapid, high-precision semantic understanding of emerging synthetic spam trends.<br>Operational data spanning a six-month period demonstrates the system's significant impact, resulting in the successful termination of 50K clusters comprising 130K channels of synthetic spam generators. Furthermore, the LLM-driven automation significantly improves operational efficiency, saving approximately 83 human review hours to cut down human reviews by 50%. This work details a critical, deployed solution that provides essential scalability and adversarial resilience against sophisticated generative attacks.

Research Areas

Machine intelligence

Anti abuse

Meet the teams driving innovation

Our teams advance the state of the art through research, systems engineering, and collaboration across Google.

See our teams

Explore our other initiatives

Google AI

Discover how Google AI is committed to enriching knowledge and solving complex challenges

Products

Build

Research

Responsibility

Societal Impact

About

Google Cloud

High-performance infrastructure for cloud computing, data analytics & machine learning

Overview

Solutions

Products

Pricing

Resources

Google DeepMind

Our mission is to build AI responsibly to benefit humanity

Models

Research

Science

About

Google Labs

Explore the future of AI responsibly with Google Labs

About

Experiments

Stay connected

About Google

Google Products

Privacy

Terms

Cookies management controls

&times;