A Deep Learning Method for Classification of Biophilic Artworks

Biophilia is an innate love for living things and nature itself that has been associated with a positive impact on mental health and well-being. This study explores the application of deep learning methods for the classification of Biophilic artwork, in order to learn and explain the different Biophilic characteristics present in a visual representation of a painting. Using the concept of Biophilia that postulates the deep connection of human beings with nature, we use an artificially intelligent algorithm to recognise the different patterns underlying the Biophilic features in an artwork. Our proposed method uses a lower-dimensional representation of an image and a decoder model to extract salient features of the image of each Biophilic trait, such as plants, water bodies, seasons, animals, etc., based on learnt factors such as shape, texture, and illumination. The proposed classification model is capable of extracting Biophilic artwork that not only helps artists, collectors, and researchers studying to interpret and exploit the effects of mental well-being on exposure to nature-inspired visual aesthetics but also enables a methodical exploration of the study of Biophilia and Biophilic artwork for aesthetic preferences. Using the proposed algorithms, we have also created a gallery of Biophilic collections comprising famous artworks from different European and American art galleries, which will soon be published on the Vieunite@ online community.

UDEEP: Edge-based Computer Vision for In-Situ Underwater Crayfish and Plastic Detection

Invasive signal crayfish have a detrimental impact on ecosystems. They spread the fungal-type crayfish plague disease (Aphanomyces astaci) that is lethal to the native white clawed crayfish, the only native crayfish species in Britain. Invasive signal crayfish extensively burrow, causing habitat destruction, erosion of river banks and adverse changes in water quality, while also competing with native species for resources and leading to declines in native populations. Moreover, pollution exacerbates the vulnerability of White-clawed crayfish, with their populations declining by over 90% in certain English counties, making them highly susceptible to extinction. To safeguard aquatic ecosystems, it is imperative to address the challenges posed by invasive species and discarded plastics in the United Kingdom's river ecosystem's. The UDEEP platform can play a crucial role in environmental monitoring by performing on-the-fly classification of Signal crayfish and plastic debris while leveraging the efficacy of AI, IoT devices and the power of edge computing (i.e., NJN). By providing accurate data on the presence, spread and abundance of these species, the UDEEP platform can contribute to monitoring efforts and aid in mitigating the spread of invasive species.

FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision

Explainability is an aspect of modern AI that is vital for impact and usability in the real world. The main objective of this paper is to emphasise the need to understand the predictions of Computer Vision models, specifically Convolutional Neural Network (CNN) based models. Existing methods of explaining CNN predictions are mostly based on Gradient-weighted Class Activation Maps (Grad-CAM) and solely focus on a single target class. We show that from the point of the target class selection, we make an assumption on the prediction process, hence neglecting a large portion of the predictor CNN model's thinking process. In this paper, we present an exhaustive methodology called Fused Multi-class Gradient-weighted Class Activation Map (FM-G-CAM) that considers multiple top predicted classes, which provides a holistic explanation of the predictor CNN's thinking rationale. We also provide a detailed and comprehensive mathematical and algorithmic description of our method. Furthermore, along with a concise comparison of existing methods, we compare FM-G-CAM with Grad-CAM, highlighting its benefits through real-world practical use cases. Finally, we present an open-source Python library with FM-G-CAM implementation to conveniently generate saliency maps for CNN-based model predictions.

Real-time Detection of AI-Generated Speech for DeepFake Voice Conversion

There are growing implications surrounding generative AI in the speech domain that enable voice cloning and real-time voice conversion from one individual to another. This technology poses a significant ethical threat and could lead to breaches of privacy and misrepresentation, thus there is an urgent need for real-time detection of AI-generated speech for DeepFake Voice Conversion. To address the above emerging issues, the DEEP-VOICE dataset is generated in this study, comprised of real human speech from eight well-known figures and their speech converted to one another using Retrieval-based Voice Conversion. Presenting as a binary classification problem of whether the speech is real or AI-generated, statistical analysis of temporal audio features through t-testing reveals that there are significantly different distributions. Hyperparameter optimisation is implemented for machine learning models to identify the source of speech. Following the training of 208 individual machine learning models over 10-fold cross validation, it is found that the Extreme Gradient Boosting model can achieve an average classification accuracy of 99.3% and can classify speech in real-time, at around 0.004 milliseconds given one second of speech. All data generated for this study is released publicly for future research on AI speech detection.

CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images

Recent technological advances in synthetic data have enabled the generation of images with such high quality that human beings cannot tell the difference between real-life photographs and Artificial Intelligence (AI) generated images. Given the critical necessity of data reliability and authentication, this article proposes to enhance our ability to recognise AI-generated images through computer vision. Initially, a synthetic dataset is generated that mirrors the ten classes of the already available CIFAR-10 dataset with latent diffusion which provides a contrasting set of images for comparison to real photographs. The model is capable of generating complex visual attributes, such as photorealistic reflections in water. The two sets of data present as a binary classification problem with regard to whether the photograph is real or generated by AI. This study then proposes the use of a Convolutional Neural Network (CNN) to classify the images into two categories; Real or Fake. Following hyperparameter tuning and the training of 36 individual network topologies, the optimal approach could correctly classify the images with 92.98% accuracy. Finally, this study implements explainable AI via Gradient Class Activation Mapping to explore which features within the images are useful for classification. Interpretation reveals interesting concepts within the image, in particular, noting that the actual entity itself does not hold useful information for classification; instead, the model focuses on small visual imperfections in the background of the images. The complete dataset engineered for this study, referred to as the CIFAKE dataset, is made publicly available to the research community for future work.

Robotic and Generative Adversarial Attacks in Offline Writer-independent Signature Verification

This study explores how robots and generative approaches can be used to mount successful false-acceptance adversarial attacks on signature verification systems. Initially, a convolutional neural network topology and data augmentation strategy are explored and tuned, producing an 87.12% accurate model for the verification of 2,640 human signatures. Two robots are then tasked with forging 50 signatures, where 25 are used for the verification attack, and the remaining 25 are used for tuning of the model to defend against them. Adversarial attacks on the system show that there exists an information security risk; the Line-us robotic arm can fool the system 24% of the time and the iDraw 2.0 robot 32% of the time. A conditional GAN finds similar success, with around 30% forged signatures misclassified as genuine. Following fine-tune transfer learning of robotic and generative data, adversarial attacks are reduced below the model threshold by both robots and the GAN. It is observed that tuning the model reduces the risk of attack by robots to 8% and 12%, and that conditional generative adversarial attacks can be reduced to 4% when 25 images are presented and 5% when 1000 images are presented.

Statistical and Spatio-temporal Hand Gesture Features for Sign Language Recognition using the Leap Motion Sensor

In modern society, people should not be identified based on their disability, rather, it is environments that can disable people with impairments. Improvements to automatic Sign Language Recognition (SLR) will lead to more enabling environments via digital technology. Many state-of-the-art approaches to SLR focus on the classification of static hand gestures, but communication is a temporal activity, which is reflected by many of the dynamic gestures present. Given this, temporal information during the delivery of a gesture is not often considered within SLR. The experiments in this work consider the problem of SL gesture recognition regarding how dynamic gestures change during their delivery, and this study aims to explore how single types of features as well as mixed features affect the classification ability of a machine learning model. 18 common gestures recorded via a Leap Motion Controller sensor provide a complex classification problem. Two sets of features are extracted from a 0.6 second time window, statistical descriptors and spatio-temporal attributes. Features from each set are compared by their ANOVA F-Scores and p-values, arranged into bins grown by 10 features per step to a limit of the 250 highest-ranked features. Results show that the best statistical model selected 240 features and scored 85.96% accuracy, the best spatio-temporal model selected 230 features and scored 80.98%, and the best mixed-feature model selected 240 features from each set leading to a classification accuracy of 86.75%. When all three sets of results are compared (146 individual machine learning models), the overall distribution shows that the minimum results are increased when inputs are any number of mixed features compared to any number of either of the two single sets of features.

Reducing Overconfidence Predictions for Autonomous Driving Perception

In state-of-the-art deep learning for object recognition, SoftMax and Sigmoid functions are most commonly employed as the predictor outputs. Such layers often produce overconfident predictions rather than proper probabilistic scores, which can thus harm the decision-making of `critical' perception systems applied in autonomous driving and robotics. Given this, the experiments in this work propose a probabilistic approach based on distributions calculated out of the Logit layer scores of pre-trained networks. We demonstrate that Maximum Likelihood (ML) and Maximum a-Posteriori (MAP) functions are more suitable for probabilistic interpretations than SoftMax and Sigmoid-based predictions for object recognition. We explore distinct sensor modalities via RGB images and LiDARs (RV: range-view) data from the KITTI and Lyft Level-5 datasets, where our approach shows promising performance compared to the usual SoftMax and Sigmoid layers, with the benefit of enabling interpretable probabilistic predictions. Another advantage of the approach introduced in this paper is that the ML and MAP functions can be implemented in existing trained networks, that is, the approach benefits from the output of the Logit layer of pre-trained networks. Thus, there is no need to carry out a new training phase since the ML and MAP functions are used in the test/prediction phase.

Improving Customer Service Chatbots with Attention-based Transfer Learning

With growing societal acceptance and increasing cost efficiency due to mass production, service robots are beginning to cross from the industrial to the social domain. Currently, customer service robots tend to be digital and emulate social interactions through on-screen text, but state-of-the-art research points towards physical robots soon providing customer service in person. This article explores two possibilities. Firstly, whether transfer learning can aid in the improvement of customer service chatbots between business domains. Secondly, the implementation of a framework for physical robots for in-person interaction. Modelled on social interaction with customer support Twitter accounts, transformer-based chatbot models are initially tasked to learn one domain from an initial random weight distribution. Given shared vocabulary, each model is then tasked with learning another domain by transferring knowledge from the prior. Following studies on 19 different businesses, results show that the majority of models are improved when transferring weights from at least one other domain, in particular those that are more data-scarce than others. General language transfer learning occurs, as well as higher-level transfer of similar domain knowledge in several cases. The chatbots are finally implemented on Temi and Pepper robots, with feasibility issues encountered and solutions are proposed to overcome them.

Continuation of Famous Art with AI: A Conditional Adversarial Network Inpainting Approach

Much of the state-of-the-art in image synthesis inspired by real artwork are either entirely generative by filtered random noise or inspired by the transfer of style. This work explores the application of image inpainting to continue famous artworks and produce generative art with a Conditional GAN. During the training stage of the process, the borders of images are cropped, leaving only the centre. An inpainting GAN is then tasked with learning to reconstruct the original image from the centre crop by way of minimising both adversarial and absolute difference losses, which are analysed by both their Fr\'echet Inception Distances and manual observations which are presented. Once the network is trained, images are then resized rather than cropped and presented as input to the generator. Following the learning process, the generator then creates new images by continuing from the edges of the original piece. Three experiments are performed with datasets of 4766 landscape paintings (impressionism and romanticism), 1167 Ukiyo-e works from the Japanese Edo period, and 4968 abstract artworks. Results show that geometry and texture (including canvas and paint) as well as scenery such as sky, clouds, water, land (including hills and mountains), grass, and flowers are implemented by the generator when extending real artworks. In the Ukiyo-e experiments, it was observed that features such as written text were generated even in cases where the original image did not have any, due to the presence of an unpainted border within the input image.