2022-05-18 更新
GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters
Authors:Anssi Kanervisto, Tomi Kinnunen, Ville Hautamäki
Playing games with cheaters is not fun, and in a multi-billion-dollar video game industry with hundreds of millions of players, game developers aim to improve the security and, consequently, the user experience of their games by preventing cheating. Both traditional software-based methods and statistical systems have been successful in protecting against cheating, but recent advances in the automatic generation of content, such as images or speech, threaten the video game industry; they could be used to generate artificial gameplay indistinguishable from that of legitimate human players. To better understand this threat, we begin by reviewing the current state of multiplayer video game cheating, and then proceed to build a proof-of-concept method, GAN-Aimbot. By gathering data from various players in a first-person shooter game we show that the method improves players’ performance while remaining hidden from automatic and manual protection mechanisms. By sharing this work we hope to raise awareness on this issue and encourage further research into protecting the gaming communities.
PDF Accepted to IEEE Transactions on Games. Source code available at https://github.com/miffyli/gan-aimbots
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VQBB: Image-to-image Translation with Vector Quantized Brownian Bridge
Authors:Bo Li, Kaitao Xue, Bin Liu, Yu-Kun Lai
Image-to-image translation is an important and challenging problem in computer vision. Existing approaches like Pixel2Pixel, DualGAN suffer from the instability of GAN and fail to generate diverse outputs because they model the task as a one-to-one mapping. Although diffusion models can generate images with high quality and diversity, current conditional diffusion models still can not maintain high similarity with the condition image on image-to-image translation tasks due to the Gaussian noise added in the reverse process. To address these issues, a novel Vector Quantized Brownian Bridge(VQBB) diffusion model is proposed in this paper. On one hand, Brownian Bridge diffusion process can model the transformation between two domains more accurate and flexible than the existing Markov diffusion methods. As far as the authors know, it is the first work for Brownian Bridge diffusion process proposed for image-to-image translation. On the other hand, the proposed method improved the learning efficiency and translation accuracy by confining the diffusion process in the quantized latent space. Finally, numerical experimental results validated the performance of the proposed method.
PDF 5 pages, 5 figures
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Applications of Deep Neural Networks with Keras
Authors:Jeff Heaton
Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Readers will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this book; however, familiarity with at least one programming language is assumed.
PDF arXiv admin note: text overlap with arXiv:1610.02357, arXiv:1603.05027, arXiv:1801.04381, arXiv:2001.02394, arXiv:1704.04861 by other authors
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Using Augmented Face Images to Improve Facial Recognition Tasks
Authors:Shuo Cheng, Guoxian Song, Wan-Chun Ma, Chao Wang, Linjie Luo
We present a framework that uses GAN-augmented images to complement certain specific attributes, usually underrepresented, for machine learning model training. This allows us to improve inference quality over those attributes for the facial recognition tasks.
PDF CHI 2022 Workshop: AI-Generated Characters: Putting Deepfakes to Good Use
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Overparameterization Improves StyleGAN Inversion
Authors:Yohan Poirier-Ginter, Alexandre Lessard, Ryan Smith, Jean-François Lalonde
Deep generative models like StyleGAN hold the promise of semantic image editing: modifying images by their content, rather than their pixel values. Unfortunately, working with arbitrary images requires inverting the StyleGAN generator, which has remained challenging so far. Existing inversion approaches obtain promising yet imperfect results, having to trade-off between reconstruction quality and downstream editability. To improve quality, these approaches must resort to various techniques that extend the model latent space after training. Taking a step back, we observe that these methods essentially all propose, in one way or another, to increase the number of free parameters. This suggests that inversion might be difficult because it is underconstrained. In this work, we address this directly and dramatically overparameterize the latent space, before training, with simple changes to the original StyleGAN architecture. Our overparameterization increases the available degrees of freedom, which in turn facilitates inversion. We show that this allows us to obtain near-perfect image reconstruction without the need for encoders nor for altering the latent space after training. Our approach also retains editability, which we demonstrate by realistically interpolating between images.
PDF 6 pages, accepted for publication at AI for Content Creation Workshop (CVPR 2022)
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