Joni Korpihalkola

Bio: Joni Korpihalkola is an academic researcher from JAMK University of Applied Sciences. The author has contributed to research in topics: Deep learning & Spot contract. The author has an hindex of 1, co-authored 6 publications receiving 5 citations.

Color-Optimized One-Pixel Attack Against Digital Pathology Images

Abstract: Modern artificial intelligence based medical imaging tools are vulnerable to model fooling attacks. Automated medical imaging methods are used for supporting the decision making by classifying samples as regular or as having characters of abnormality. One use of such technology is the analysis of whole-slide image tissue samples. Consequently, attacks against artificial intelligence based medical imaging methods may diminish the credibility of modern diagnosis methods and, at worst, may lead to misdiagnosis with improper treatment. This study demonstrates an advanced color-optimized one-pixel attack against medical imaging. A state-of-the-art one-pixel modification is constructed with minimal effect on the pixel's color value. This multi-objective approach mitigates the unnatural coloring of raw none-pixel attacks. Accordingly, it is infeasible or at least cumbersome for a human to see the modification in the image under analysis. This color-optimized one-pixel attack poses an advanced cyber threat against modern medical imaging and shows the importance of data integrity with image analysis.

One-Pixel Attack Deceives Computer-Assisted Diagnosis of Cancer

Abstract: Computer vision and machine learning can be used to automate various tasks in cancer diagnostic and detection. If an attacker can manipulate the automated processing, the results can be devastating and in the worst case lead to wrong diagnosis and treatment. In this research, the goal is to demonstrate the use of one-pixel attacks in a real-life scenario with a real pathology dataset, TUPAC16, which consists of digitized whole-slide images. We attack against the IBM CODAIT's MAX breast cancer detector using adversarial images. These adversarial examples are found using differential evolution to perform the one-pixel modification to the images in the dataset. The results indicate that a minor one-pixel modification of a whole slide image under analysis can affect the diagnosis by reversing the automatic diagnosis result. The attack poses a threat from the cyber security perspective: the one-pixel method can be used as an attack vector by a motivated attacker.

One-Pixel Attack Deceives Automatic Detection of Breast Cancer.

Abstract: In this article we demonstrate that a state-of-the-art machine learning model predicting whether a whole slide image contains mitosis can be fooled by changing just a single pixel in the input image. Computer vision and machine learning can be used to automate various tasks in cancer diagnostic and detection. If an attacker can manipulate the automated processing, the results can be devastating and in the worst case lead to wrong diagnostic and treatments. In this research one-pixel attack is demonstrated in a real-life scenario with a real tumor dataset. The results indicate that a minor one-pixel modification of a whole slide image under analysis can affect the diagnosis. The attack poses a threat from the cyber security perspective: the one-pixel method can be used as an attack vector by a motivated attacker.

One-Pixel Attack Deceives Computer-Assisted Diagnosis of Cancer

Abstract: Computer vision and machine learning can be used to automate various tasks in cancer diagnostic and detection. If an attacker can manipulate the automated processing, the results can be devastating and in the worst case lead to wrong diagnosis and treatment. In this research, the goal is to demonstrate the use of one-pixel attacks in a real-life scenario with a real pathology dataset, TUPAC16, which consists of digitized whole-slide images. We attack against the IBM CODAIT's MAX breast cancer detector using adversarial images. These adversarial examples are found using differential evolution to perform the one-pixel modification to the images in the dataset. The results indicate that a minor one-pixel modification of a whole slide image under analysis can affect the diagnosis by reversing the automatic diagnosis result. The attack poses a threat from the cyber security perspective: the one-pixel method can be used as an attack vector by a motivated attacker.

Prediction of Spot Prices in Nord Pool’s Day-Ahead Market Using Machine Learning and Deep Learning

Abstract: Aim of this paper is to describe and compare the machine learning and deep learning based forecasting models that predict Spot prices in Nord Pool’s Day-ahead market in Finland with open-source software. The liberalization of electricity markets has launched an interest in forecasting future prices and developing models on how the prices will develop. Due to the improvements in computing capabilities, more and more complex machine learning models and neural networks can be trained faster as well as the growing amount of open data enables to collect of the large and relevant dataset. The dataset consist of multiple different features ranging from weather data to production plans was constructed. Different statistical models generated forecasts from Spot price history and machine learning models were trained on the constructed dataset. The forecasts were compared to a baseline model using three different error metrics. The result was an ensemble of statistical and machine learning models, where the models’ forecasts were combined and given weights by a neural network acting as a metalearner. The results also prove that the model is able to forecast the trend and seasonality of Spot prices but unable to predict sudden price spikes.

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx.

Abstract: We extend the neural basis expansion analysis (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well performing deep learning model, extending its capabilities by including exogenous variables and allowing it to integrate multiple sources of useful information. To showcase the utility of the NBEATSx model, we conduct a comprehensive study of its application to electricity price forecasting (EPF) tasks across a broad range of years and markets. We observe state-of-the-art performance, significantly improving the forecast accuracy by nearly 20% over the original NBEATS model, and by up to 5% over other well established statistical and machine learning methods specialized for these tasks. Additionally, the proposed neural network has an interpretable configuration that can structurally decompose time series, visualizing the relative impact of trend and seasonal components and revealing the modeled processes' interactions with exogenous factors. To assist related work we made the code available in this https URL.

Color-Optimized One-Pixel Attack Against Digital Pathology Images

Abstract: Modern artificial intelligence based medical imaging tools are vulnerable to model fooling attacks. Automated medical imaging methods are used for supporting the decision making by classifying samples as regular or as having characters of abnormality. One use of such technology is the analysis of whole-slide image tissue samples. Consequently, attacks against artificial intelligence based medical imaging methods may diminish the credibility of modern diagnosis methods and, at worst, may lead to misdiagnosis with improper treatment. This study demonstrates an advanced color-optimized one-pixel attack against medical imaging. A state-of-the-art one-pixel modification is constructed with minimal effect on the pixel's color value. This multi-objective approach mitigates the unnatural coloring of raw none-pixel attacks. Accordingly, it is infeasible or at least cumbersome for a human to see the modification in the image under analysis. This color-optimized one-pixel attack poses an advanced cyber threat against modern medical imaging and shows the importance of data integrity with image analysis.

Chromatic and Spatial Analysis of One-Pixel Attacks Against an Image Classifier

Abstract: One-pixel attack is a curious way of deceiving neural network classifier by changing only one pixel in the input image. The full potential and boundaries of this attack method are not yet fully understood. In this research, the successful and unsuccessful attacks are studied in more detail to illustrate the working mechanisms of a one-pixel attack created using differential evolution. The data comes from our earlier studies where we applied the attack against medical imaging. We used a real breast cancer tissue dataset and a real classifier as the attack target. This research presents ways to analyze chromatic and spatial distributions of one-pixel attacks. In addition, we present one-pixel attack confidence maps to illustrate the behavior of the target classifier. We show that the more effective attacks change the color of the pixel more, and that the successful attacks are situated at the center of the images. This kind of analysis is not only useful for understanding the behavior of the attack but also the qualities of the classifying neural network.

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions.

Abstract: Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges. These include high inter-observer variability, difficulty of small-sized lesion detection, nodule interpretation and malignancy determination, inter- and intra-tumour heterogeneity, class imbalance, segmentation inaccuracies, and treatment effect uncertainty. The recent advancements in Generative Adversarial Networks (GANs) in computer vision as well as in medical imaging may provide a basis for enhanced capabilities in cancer detection and analysis. In this review, we assess the potential of GANs to address a number of key challenges of cancer imaging, including data scarcity and imbalance, domain and dataset shifts, data access and privacy, data annotation and quantification, as well as cancer detection, tumour profiling and treatment planning. We provide a critical appraisal of the existing literature of GANs applied to cancer imagery, together with suggestions on future research directions to address these challenges. We analyse and discuss 163 papers that apply adversarial training techniques in the context of cancer imaging and elaborate their methodologies, advantages and limitations. With this work, we strive to bridge the gap between the needs of the clinical cancer imaging community and the current and prospective research on GANs in the artificial intelligence community.