Adversarial image generation using geneticalgorithms with black-box technique - Pérez, Gabriela Alejandra (2023)
Convolutional neural networks are a technique that has demon-strated great success in computer vision tasks, such as image classifica-tion and object detection. Like any machine learning model, they havelimitations and vulnerabilities that must be carefully considered for safeand effective use. One of the main limitations lies in their complexityand the difficulty of interpreting their internal workings, which can beexploited for malicious purposes. The goal of these attacks is to makedeliberate changes to the input data in order to deceive the model andcause it to make incorrect decisions. These attacks are known as adver-sarial attacks. This work focuses on the generation of adversarial im-ages using genetic algorithms for a convolutional neural network trainedon the MNIST dataset. Several strategies are employed, including tar-geted and untargeted attacks, as well as the presentation of interpretableand non-interpretable images that are unrecognizable to humans but aremisidentified and confidently classified by the network. The experimentdemonstrates the ability to generate adversarial images in a relativelyshort time, highlighting the vulnerability of neural networks and the easewith which they can be deceived. These results underscore the impor-tance of developing more secure and reliable artificial intelligence systemscapable of resisting such attacks