# Scissorhands: Scrub Data Influence via Connection Sensitivity in Networks

arXiv: https://arxiv.org/abs/2401.06187 · HTML: https://mlsanity.com/paper/2401.06187/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Placed in Machine unlearning, one of 12 directions in LLM safety and security.
- Builds on 54 of its 75 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Placed in

- LLM safety and security / [Machine unlearning](https://mlsanity.com/safety/machine-unlearning/)

## Builds on (most cited first, 30 of 54)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [SNIP: Single-shot Network Pruning based on Connection Sensitivity](https://mlsanity.com/paper/1810.02340/) (2018)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks](https://mlsanity.com/paper/1911.04933/) (2019)
- [Erasing Concepts from Diffusion Models](https://mlsanity.com/paper/2303.07345/) (2023)
- [Certified Data Removal from Machine Learning Models](https://mlsanity.com/paper/1911.03030/) (2019)
- [Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models](https://mlsanity.com/paper/2211.05105/) (2022)
- [Making AI Forget You: Data Deletion in Machine Learning](https://mlsanity.com/paper/1907.05012/) (2019)
- [Unified Concept Editing in Diffusion Models](https://mlsanity.com/paper/2308.14761/) (2023)
- [SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation](https://mlsanity.com/paper/2310.12508/) (2023)
- [Remember What You Want to Forget: Algorithms for Machine Unlearning](https://mlsanity.com/paper/2103.03279/) (2021)
- [A Survey of Machine Unlearning](https://mlsanity.com/paper/2209.02299/) (2022)
- [Descent-to-Delete: Gradient-Based Methods for Machine Unlearning](https://mlsanity.com/paper/2007.02923/) (2020)
- [Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2303.17591/) (2023)
- [Unrolling SGD: Understanding Factors Influencing Machine Unlearning](https://mlsanity.com/paper/2109.13398/) (2021)
- [Red-Teaming the Stable Diffusion Safety Filter](https://mlsanity.com/paper/2210.04610/) (2022)
- [Approximate Data Deletion from Machine Learning Models](https://mlsanity.com/paper/2002.10077/) (2020)
- [Large image datasets: A pyrrhic win for computer vision?](https://mlsanity.com/paper/2006.16923/) (2020)
- [Machine Unlearning of Features and Labels](https://mlsanity.com/paper/2108.11577/) (2021)
- [Fast Yet Effective Machine Unlearning](https://mlsanity.com/paper/2111.08947/) (2021)

## Built on by (most cited first, 30 of 52)

- [Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models](https://mlsanity.com/paper/2405.15234/) (2024)
- [SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders](https://mlsanity.com/paper/2501.18052/) (2025)
- [ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron Pruning](https://mlsanity.com/paper/2405.19237/) (2024)
- [UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models](https://mlsanity.com/paper/2402.11846/) (2024)
- [Learning to Unlearn while Retaining: Combating Gradient Conflicts in Machine Unlearning](https://mlsanity.com/paper/2503.06339/) (2025)
- [Targeted Unlearning with Single Layer Unlearning Gradient](https://mlsanity.com/paper/2407.11867/) (2024)
- [MUNBa: Machine Unlearning via Nash Bargaining](https://mlsanity.com/paper/2411.15537/) (2024)
- [One Image is Worth a Thousand Words: A Usability Preservable Text-Image Collaborative Erasing Framework](https://mlsanity.com/paper/2505.11131/) (2025)
- [Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models](https://mlsanity.com/paper/2410.08074/) (2024)
- [Memories of Forgotten Concepts](https://mlsanity.com/paper/2412.00782/) (2024)
- [SAEmnesia: Erasing Concepts in Diffusion Models with Supervised Sparse Autoencoders](https://mlsanity.com/paper/2509.21379/) (2025)
- [Rethinking Machine Unlearning in Image Generation Models](https://mlsanity.com/paper/2506.02761/) (2025)
- [CE-SDWV: Effective and Efficient Concept Erasure for Text-to-Image Diffusion Models via a Semantic-Driven Word Vocabulary](https://mlsanity.com/paper/2501.15562/) (2025)
- [Sparse Autoencoder as a Zero-Shot Classifier for Concept Erasing in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2503.09446/) (2025)
- [RAZOR: Ratio-Aware Layer Editing for Targeted Unlearning in Vision Transformers and Diffusion Models](https://mlsanity.com/paper/2603.14819/) (2026)
- [Go Beyond Your Means: Unlearning with Per-Sample Gradient Orthogonalization](https://mlsanity.com/paper/2503.02312/) (2025)
- [UnGuide: Learning to Forget with LoRA-Guided Diffusion Models](https://mlsanity.com/paper/2508.05755/) (2025)
- [Concept Unlearning by Modeling Key Steps of Diffusion Process](https://mlsanity.com/paper/2507.06526/) (2025)
- [Reference-Specific Unlearning Metrics Can Hide the Truth: A Reality Check](https://mlsanity.com/paper/2510.12981/) (2025)
- [Locality-Aware Continual Unlearning for Diffusion Models](https://mlsanity.com/paper/2512.02657/) (2025)
- [AEGIS: Adversarial Target-Guided Retention-Data-Free Robust Concept Erasure from Diffusion Models](https://mlsanity.com/paper/2602.06771/) (2026)
- [EMMA: Concept Erasure Benchmark with Comprehensive Semantic Metrics and Diverse Categories](https://mlsanity.com/paper/2512.17320/) (2025)
- [TINA: Text-Free Inversion Attack for Unlearned Text-to-Image Diffusion Models](https://mlsanity.com/paper/2603.17828/) (2026)
- [Module-Aware Parameter-Efficient Machine Unlearning on Transformers](https://mlsanity.com/paper/2508.17233/) (2025)
- [UnHype: CLIP-Guided Hypernetworks for Dynamic LoRA Unlearning](https://mlsanity.com/paper/2602.03410/) (2026)
- [Localized Concept Erasure in Text-to-Image Diffusion Models via High-Level Representation Misdirection](https://mlsanity.com/paper/2602.19631/) (2026)
- [Rethinking the Vulnerability of Concept Erasure and a New Method](https://mlsanity.com/paper/2502.17537/) (2025)
- [Memory Self-Regeneration: Uncovering Hidden Knowledge in Unlearned Models](https://mlsanity.com/paper/2510.03263/) (2025)
- [SPQR: A Multi-Dimensional Benchmark for Safety Alignment under Benign Model Adaptation](https://mlsanity.com/paper/2511.19558/) (2025)
- [Disentangled Sparse Representations for Concept-Separated Diffusion Unlearning](https://mlsanity.com/paper/2605.12122/) (2026)

## Abstract (arXiv)

Machine unlearning has become a pivotal task to erase the influence of data from a trained model. It adheres to recent data regulation standards and enhances the privacy and security of machine learning applications. In this work, we present a new machine unlearning approach Scissorhands. Initially, Scissorhands identifies the most pertinent parameters in the given model relative to the forgetting data via connection sensitivity. By reinitializing the most influential top-k percent of these parameters, a trimmed model for erasing the influence of the forgetting data is obtained. Subsequently, Scissorhands fine-tunes the trimmed model with a gradient projection-based approach, seeking parameters that preserve information on the remaining data while discarding information related to the forgetting data. Our experimental results, conducted across image classification and image generation tasks, demonstrate that Scissorhands, showcases competitive performance when compared to existing methods. Source code is available at https://github.com/JingWu321/Scissorhands.