Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash

Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash

12 November 2021

Dominik Hintersdorf

Kristian Kersting

Papers citing "Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash"

8 / 8 papers shown

Title
Minimizing Risk Through Minimizing Model-Data Interaction: A Protocol For Relying on Proxy Tasks When Designing Child Sexual Abuse Imagery Detection Models Thamiris Coelho Leo S. F. Ribeiro João Macedo J. A. dos Santos Sandra Avila 29 0 0 10 May 2025
Robust, privacy-preserving, transparent, and auditable on-device blocklisting Kurt Thomas Sarah Meiklejohn Michael A. Specter Xiang Wang Xavier Llorà Stephan Somogyi D. Kleidermacher 11 2 0 06 Apr 2023
Poisoning Web-Scale Training Datasets is Practical Nicholas Carlini Matthew Jagielski Christopher A. Choquette-Choo Daniel Paleka Will Pearce Hyrum S. Anderson Andreas Terzis Kurt Thomas Florian Tramèr SILM 31 182 0 20 Feb 2023
Re-purposing Perceptual Hashing based Client Side Scanning for Physical Surveillance Ashish Hooda Andrey Labunets Tadayoshi Kohno Earlence Fernandes 19 2 0 08 Dec 2022
Does CLIP Know My Face? Dominik Hintersdorf Lukas Struppek Manuel Brack Felix Friedrich P. Schramowski Kristian Kersting VLM 21 9 0 15 Sep 2022
Combining AI and AM - Improving Approximate Matching through Transformer Networks Frieder Uhlig Lukas Struppek Dominik Hintersdorf Thomas Gobel Harald Baier Kristian Kersting 21 7 0 24 Aug 2022
Bugs in our Pockets: The Risks of Client-Side Scanning H. Abelson Ross J. Anderson S. Bellovin Josh Benaloh M. Blaze ... Ronald L. Rivest J. Schiller B. Schneier Vanessa J. Teague Carmela Troncoso 62 39 0 14 Oct 2021
A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras S. Laine Timo Aila 297 10,368 0 12 Dec 2018