Knockoff Nets: Stealing Functionality of Black-Box Models

Knockoff Nets: Stealing Functionality of Black-Box Models

6 December 2018

Tribhuvanesh Orekondy

Bernt Schiele

Mario Fritz

Papers citing "Knockoff Nets: Stealing Functionality of Black-Box Models"

7 / 107 papers shown

Title
Model Weight Theft With Just Noise Inputs: The Curious Case of the Petulant Attacker Nicholas Roberts Vinay Uday Prabhu Matthew McAteer 8 18 0 19 Dec 2019
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples Nils Lukas Yuxuan Zhang Florian Kerschbaum MLAU FedML AAML 31 144 0 02 Dec 2019
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey Olakunle Ibitoye Rana Abou-Khamis Mohamed el Shehaby Ashraf Matrawy M. O. Shafiq AAML 34 68 0 06 Nov 2019
Extraction of Complex DNN Models: Real Threat or Boogeyman? B. Atli S. Szyller Mika Juuti Samuel Marchal Nadarajah Asokan MLAU MIACV 30 45 0 11 Oct 2019
Universal Adversarial Audio Perturbations Sajjad Abdoli L. G. Hafemann Jérôme Rony Ismail Ben Ayed P. Cardinal Alessandro Lameiras Koerich AAML 25 51 0 08 Aug 2019
Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks Tribhuvanesh Orekondy Bernt Schiele Mario Fritz AAML 11 164 0 26 Jun 2019
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning Tribhuvanesh Orekondy Seong Joon Oh Yang Zhang Bernt Schiele Mario Fritz PICV FedML 359 37 0 15 May 2018