Artifact for article: PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations

2022 ◽  
Author(s):  
Mark Niklas Müller ◽  
Gleb Makarchuk ◽  
Gagandeep Singh ◽  
Markus Püschel ◽  
Martin Vechev
Keyword(s):  
Neural Network ◽  
Convex Hull

scholarly journals Signature Verification Using Support Vector Machine and Convolution Neural Network

2021 ◽  
Vol 12 (1S) ◽  
pp. 80-89
Author(s):  
Kritika Vohra, Et. al.
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Aspect Ratio ◽  
Convex Hull ◽  
Unique Feature ◽  
Signature Verification ◽  
Support Vector ◽  
Individual Signature ◽  
Business Functions

Signature is used for recognition of an individual. Signature is considered as a mark that an individual write on a paper for his/her identity or proof. It is used as a unique feature for identifying an individual. It is highly used in social and business functions which gives rise to verification of signature. There are chances of signature getting forged. Hence, the need to identify signature as genuine of forged is utmost important. In this paper, identification of signature as genuine or forged is done using two approaches. First approach is using SVM and second is using CNN. For SVM, pre-processing of signature image is done and feature extraction is performed. Features extracted are histogram of gradient, shape, aspect ratio, bounding area, contour area and convex hull area. Further, SVM is applied to classify signature as genuine or forged and accuracy is determined. In the second approach, signature image is pre-processed, CNN is used to classify signature as genuine or forged and accuracy is determined. Dataset used here is ICDAR Dutch dataset along with 80 signatures taken from 4 people.Dutch dataset consists of 362 signature imagesand signature images taken from 4 people consists 10 genuine and 10 forged signatures which sums to 442 signature images. The proposed system provides accuracy of 86.39% using SVM and around 83.78% using CNN.

scholarly journals PRIMA: general and precise neural network certification via scalable convex hull approximations

2022 ◽  
Vol 6 (POPL) ◽  
pp. 1-33
Author(s):  
Mark Niklas Müller ◽  
Gleb Makarchuk ◽  
Gagandeep Singh ◽  
Markus Püschel ◽  
Martin Vechev
Keyword(s):  
Neural Network ◽  
Convex Hull ◽  
Autonomous Driving ◽  
Activation Function ◽  
Network Architectures ◽  
Major Step ◽  
Challenging Tasks ◽  
Real World Applications ◽  
Difficult Challenge ◽  
First Time

Formal verification of neural networks is critical for their safe adoption in real-world applications. However, designing a precise and scalable verifier which can handle different activation functions, realistic network architectures and relevant specifications remains an open and difficult challenge. In this paper, we take a major step forward in addressing this challenge and present a new verification framework, called PRIMA. PRIMA is both (i) general: it handles any non-linear activation function, and (ii) precise: it computes precise convex abstractions involving multiple neurons via novel convex hull approximation algorithms that leverage concepts from computational geometry. The algorithms have polynomial complexity, yield fewer constraints, and minimize precision loss. We evaluate the effectiveness of PRIMA on a variety of challenging tasks from prior work. Our results show that PRIMA is significantly more precise than the state-of-the-art, verifying robustness to input perturbations for up to 20%, 30%, and 34% more images than existing work on ReLU-, Sigmoid-, and Tanh-based networks, respectively. Further, PRIMA enables, for the first time, the precise verification of a realistic neural network for autonomous driving within a few minutes.

A NOVEL CONNECTIONIST FRAMEWORK FOR COMPUTATION OF AN APPROXIMATE CONVEX-HULL OF A SET OF PLANAR POINTS, CIRCLES AND ELLIPSES

2006 ◽  
Vol 16 (01) ◽  
pp. 15-28 ◽  
Author(s):  
SRIMANTA PAL ◽  
SABYASACHI BHATTACHARYA ◽  
NIKHIL R. PAL
Keyword(s):  
Neural Network ◽  
Convex Hull ◽  
Rubber Band ◽  
Parameter Vector ◽  
Current Position ◽  
Direction Of Movement ◽  
Set Of Points ◽  
The Given ◽  
As If

We propose a two layer neural network for computation of an approximate convex-hull of a set of points or a set of circles/ellipses of different sizes. The algorithm is based on a very elegant concept — shrinking of a rubber band surrounding the set of planar objects. Logically, a set of neurons is placed on a circle (rubber band) surrounding the objects. Each neuron has a parameter vector associated with it. This may be viewed as the current position of the neuron. The given set of points/objects exerts a force of attraction on every neuron, which determines how its current position will be updated (as if, the force determines the direction of movement of the neuron lying on the rubber band). As the network evolves, the neurons (parameter vectors) approximate the convex-hull more and more accurately. The scheme can be applied to find the convex-hull of a planar set of circles or ellipses or a mixture of the two. Some properties related to the evolution of the algorithm are also presented.

scholarly journals Combination of Morphology, Wavelet and Convex Hull Features in Classification of Patchouli Varieties with Imbalance Data using Artificial Neural Network

2021 ◽  
Vol 19 (6) ◽  
pp. 633-643
Author(s):  
Wayan Firdaus Mahmudy ◽  
Candra Dewi ◽  
Rio Arifando ◽  
Beryl Labique Ahmadie ◽  
Muh Arif Rahman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Convex Hull ◽  
Morphological Features ◽  
Variety Identification ◽  
Identification System ◽  
Imbalance Data ◽  
Average Accuracy ◽  
Physical Form ◽  
Artificial Neural

Patchouli plants are main raw materials for essential oils in Indonesia. Patchouli leaves have a very varied physical form based on the area planted, making it difficult to recognize the variety. This condition makes it difficult for farmers to recognize these varieties and they need experts’ advice. As there are few experts in this field, a technology for identifying the types of patchouli varieties is required. In this study, the identification model is constructed using a combination of leaf morphological features, texture features extracted with Wavelet and shape features extracted with convex hull. The results of feature extraction are used as input data for training of classification algorithms. The effectiveness of the input features is tested using three classification methods in class artificial neural network algorithms: (1) feedforward neural networks with backpropagation algorithm for training, (2) learning vector quantization (LVQ), (3) extreme learning machine (ELM). Synthetic minority over-sampling technique (SMOTE) is applied to solve the problem of class imbalance in the patchouli variety dataset. The results of the patchouli variety identification system by combining these three features indicate the level of recognition with an average accuracy of 72.61%, accuracy with the combination of these three features is higher when compared to using only morphological features (58.68%) or using only Wavelet features (59.03 %) or both (67.25%). In this study also showed that the use of SMOTE in imbalance data increases the accuracy with the highest average accuracy of 88.56%.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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