With the rise of the so-called Adversarial Attacks, there
is an increased concern on model security. In this paper we
present two different contributions: novel measures of
robustness (based on adversarial attacks) and a novel
adversarial attack. The key idea behind these metrics is to
obtain a measure that could compare different
architectures, with independence of how the input is
preprocessed (robustness against different input sizes and
value ranges). To do so, a novel adversarial attack is
presented, performing a delayed elastic-net adversarial
attack (constraints are only used whenever a successful
adversarial attack is obtained). Experimental results show
that our approach obtains state-of-the-art adversarial
samples, in terms of minimal perturbation distance.
Finally, a benchmark of ImageNet pretrained models is used
to conduct experiments aiming to shed some light about
which model should be selected whenever security is a role
factor.

A Delayed Elastic-Net Approach for Performing Adversarial Attacks

Traditionally, a Neural Network classification training loss function follows the same principle: minimizing the distance between samples that belong to the same class, while maximizing the distance to the other classes. There are no restrictions on the spatial placement of deep features (last layer input). This paper addresses this issue when dealing with Neural Networks, providing a set of loss functions that are able to train a classifier by forcing the deep features to be projected over a predefined orthogonal basis. Experimental results shows that these `data placement' functions can overcome the training accuracy provided by the classic cross-entropy loss function.

Can Data Placement Be Effective for Neural Networks Classification Tasks? Introducing the Orthogonal Loss

**Session Chair:** prof. Roberto Vezzani


PS T1.5: Pattern Recognition and Neural Network Applications

poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


Brais Cancela

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Presentations

A Delayed Elastic-Net Approach for Performing Adversarial Attacks

Can Data Placement Be Effective for Neural Networks Classification Tasks? Introducing the Orthogonal Loss

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