ΑΙhub.org
 

#ICML2022 Test of Time award announced

by
20 July 2022



share this:
winners' medal

The International Conference on Machine Learning (ICML) Test of Time award is given to a paper from ICML ten years ago that has had significant impact. This year the award goes to:

Poisoning Attacks against Support Vector Machines
Battista Biggio, Blaine Nelson and Pavel Laskov

The paper investigates adversarial machine learning and, specifically, poisoning attacks on support vector machines (SVMs). The awards committee noted that this paper is one of the earliest and most impactful papers on the theme of poisoning attacks, which are now widely studied by the community. The authors use a gradient ascent strategy in which the gradient is computed based on properties of the SVM’s optimal solution. The method can be kernelized, thereby not needing explicit feature representation. The committee judged that this paper initiated thorough investigation of the problem and inspired significant subsequent work.

The abstract of the paper:
We investigate a family of poisoning attacks against Support Vector Machines (SVM). Such attacks inject specially crafted training data that increases the SVM’s test error. Central to the motivation for these attacks is the fact that most learning algorithms assume that their training data comes from a natural or well-behaved distribution. However, this assumption does not generally hold in security-sensitive settings. As we demonstrate, an intelligent adversary can, to some extent, predict the change of the SVM’s decision function due to malicious input and use this ability to construct malicious data. The proposed attack uses a gradient ascent strategy in which the gradient is computed based on properties of the SVM’s optimal solution. This method can be kernelized and enables the attack to be constructed in the input space even for non-linear kernels. We experimentally demonstrate that our gradient ascent procedure reliably identifies good local maxima of the non-convex validation error surface, which significantly increases the classifier’s test error.

You can read the paper in full here.

In a special award session, first author Battista Biggio gave a plenary talk describing the research, and subsequent developments. In the early days of this research field (around 2006-2010) work on poisoning attacks focussed on simple models and concerned security-related applications, such as spam filtering and network intrusion detection. The challenge that the authors set themselves with this work was to find out if these attacks were possible against a more complex classifier, and something closer to the state-of-the-art. They decided to study SVMs because they were theoretically grounded and quite popular at the time. Their strategy was to find an optimal attack point (to inject the “poisoned” samples) that maximised the classification error.

Battista also spoke about some of the work that has followed in this space, including adversarial examples for deep neural networks, machine learning security, and different types of attacks against machine learning models.

You can watch Battista’s original talk on this paper, at ICML 2012.

There were also two Test of Time honourable mentions:

  • Building high-level features using large scale unsupervised learning
    Quoc Le, Marc’Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg Corrado, Jeff Dean, Andrew Ng
    Read the paper here.
  • On causal and anticausal learning
    Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris Mooij
    Read the paper here.


tags: ,


Lucy Smith , Managing Editor for AIhub.
Lucy Smith , Managing Editor for AIhub.




            AIhub is supported by:


Related posts :



Madagascar’s ancient baobab forests are being restored by communities – with a little help from AI

The collaboration between communities and scientists aims to restore baobab forests in Madagascar to their natural state.
24 May 2024, by

DataLike: Interview with Wuraola Oyewusi

Ndane and Isabella talk to Wuraola Oyewusi about challenging and rewarding aspects of research and how her background in pharmacy has helped her data and AI career

European Union AI Act receives final approval

On 21 May, the Council of the EU formally signed off the artificial intelligence Act.
22 May 2024, by

#ICLR2024 invited talk: Priya Donti on why your work matters for climate more than you think

How is AI research related to climate, and how can the AI community better align their work with climate change-related goals?
21 May 2024, by

Congratulations to the #ICRA2024 best paper winners

The winners and finalists in the different categories have been announced.
20 May 2024, by

Trotting robots offer insights into animal gait transitions

A four-legged robot trained with machine learning has learned to avoid falls by spontaneously switching between walking, trotting, and pronking
17 May 2024, by




AIhub is supported by:






©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association