Guided Diffusion: Crafting Potent Poisons and Backdoors from Scratch

Core Concepts

Neural networks can be vulnerable to poisoning attacks, but crafting base samples with guided diffusion can lead to potent poisons and backdoors.

Abstract

• Modern neural networks are at risk of poisoning attacks due to insecure curation pipelines.
• Crafting base samples with guided diffusion can enhance the effectiveness of poisoning attacks.
• GDP base samples boost existing state-of-the-art targeted data poisoning and backdoor attacks.
• The method involves generating base samples, initializing attacks, and filtering poisons for optimal results.
• Human evaluation confirms that GDP base samples maintain their clean-label status effectively.

Stats

Modern neural networks are often trained on massive datasets that are web scraped with minimal human inspection. As a result of this insecure curation pipeline, an adversary can poison or backdoor the resulting model by uploading malicious data to the internet and waiting for a victim to scrape and train on it.
Existing approaches for creating poisons and backdoors start with randomly sampled clean data, called base samples, and then modify those samples to craft poisons.
In our experiments on CIFAR-10, injecting only 25-50 poisoned samples was enough for the attack to be effective. On ImageNet, modifying only a tiny subset of training images (0.004%-0.008%) was sufficient to poison the model effectively.

Quotes

"Crafting base samples from scratch allows us to optimize them specifically for the poisoning objective."
"Our approach amplifies the effects of state-of-the-art targeted data poisoning and backdoor attacks across multiple datasets."
"GDP outperforms all existing backdoor attacks in our experiments."

Key Insights Distilled From

Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion

by Hossein Sour... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16365.pdf

Deeper Inquiries

How can we ensure the scalability of crafting potent poisons without relying on diffusion models

攻撃的な毒を作成する際の拡張性を確保するために、拡散モデルに頼らずに効果的な毒を生成する方法が必要です。一つのアプローチは、汎用テキストから画像への変換モデルを使用して、特定のプロンプトを適切に設定し、特定のデータセット固有の拡散モデルをトレーニングせずに済むような手法です。このような一般的なモデルは異なるデータセットやタスクで再利用可能であり、スケーラビリティと柔軟性が向上します。

What are potential implications of using general-purpose text-to-image diffusion models for crafting base samples

基本サンプル生成時に汎用テキストから画像への拡散モデルを使用した場合の潜在的影響は大きいです。このアプローチでは、特定のデータ分布ごとに個別に訓練された拡散モデルが不要となります。代わりに適切な提示文言で一般目的テキストから画像へ変換することで、さまざまなタスクやドメインでも活用可能です。ただし、パフォーマンスや精度面で課題もあるため注意が必要です。

How can we optimize the process of generating poisons more efficiently without needing extensive filtering

効率良く毒物質生成プロセスを最適化しフィルタリング作業を最小限化する方法はいくつか考えられます。例えば、「発生」段階で生成される毒素数自体を制御し余剰分析後処理工程（フィルタリング）回避すれば効率向上します。「発生」段階では高品質・ポイントラベリングした基本サンプルだけ残すことで無駄が省けます。