2022 Conference article Open Access

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2022 Conference article Open Access

See at: ISTI Repository | www.ital-ia2022.it | CNR ExploRA

2022 Conference article Open Access

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2022 Journal article Open Access

A leap among quantum computing and quantum neural networks: a survey
Massoli F. V., Vadicamo L., Amato G., Falchi F.
In recent years, Quantum Computing witnessed massive improvements in terms of available resources and algorithms development. The ability to harness quantum phenomena to solve computational problems is a long-standing dream that has drawn the scientific community's interest since the late 80s. In such a context, we propose our contribution. First, we introduce basic concepts related to quantum computations, and then we explain the core functionalities of technologies that implement the Gate Model and Adiabatic Quantum Computing paradigms. Finally, we gather, compare and analyze the current state-of-the-art concerning Quantum Perceptrons and Quantum Neural Networks implementations.Source: ACM computing surveys (2022). doi:10.1145/3529756
DOI: 10.1145/3529756
DOI: 10.48550/arxiv.2107.03313
Project(s): AI4EU , AI4Media
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See at: arXiv.org e-Print Archive | ISTI Repository | ACM Computing Surveys | doi.org | CNR ExploRA

2021 Journal article Open Access

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2021 Conference article Open Access

A multi-resolution training for expression recognition in the wild
Massoli F. V., Cafarelli D., Amato G., Falchi F.
Facial expressions play a fundamental role in human communication, and their study, which represents a multidisciplinary subject, embraces a great variety of research fields, e.g., from psychology to computer science, among others. Concerning Deep Learning, the recognition of facial expressions is a task named Facial Expression Recognition (FER). With such an objective, the goal of a learning model is to classify human emotions starting from a facial image of a given subject. Typically, face images are acquired by cameras that have, by nature, different characteristics, such as the output resolution. Moreover, other circumstances might involve cameras placed far from the observed scene, thus obtaining faces with very low resolutions. Therefore, since the FER task might involve analyzing face images that can be acquired with heterogeneous sources, it is plausible to expect that resolution plays a vital role. In such a context, we propose a multi-resolution training approach to solve the FER task. We ground our intuition on the observation that, often, face images are acquired at different resolutions. Thus, directly considering such property while training a model can help achieve higher performance on recognizing facial expressions. To our aim, we use a ResNet-like architecture, equipped with Squeeze-and-Excitation blocks, trained on the Affect-in-the-Wild 2 dataset. Not being available a test set, we conduct tests and model selection by employing the validation set only on which we achieve more than 90% accuracy on classifying the seven expressions that the dataset comprises.Source: SEBD 2021 - Italian Symposium on Advanced Database Systems, pp. 427–433, Pizzo Calabro, 5-9/9/2021
Project(s): AI4EU

See at: ceur-ws.org | ISTI Repository | CNR ExploRA

2021 Report Open Access

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2021 Journal article Open Access

MOCCA: multilayer one-class classification for anomaly detection
Massoli F. V., Falchi F., Kantarci A., Akti S., Ekenel H. K., Amato G.
Anomalies are ubiquitous in all scientific fields and can express an unexpected event due to incomplete knowledge about the data distribution or an unknown process that suddenly comes into play and distorts the observations. Usually, due to such events' rarity, to train deep learning (DL) models on the anomaly detection (AD) task, scientists only rely on "normal" data, i.e., nonanomalous samples. Thus, letting the neural network infer the distribution beneath the input data. In such a context, we propose a novel framework, named multilayer one-class classification (MOCCA), to train and test DL models on the AD task. Specifically, we applied our approach to autoencoders. A key novelty in our work stems from the explicit optimization of the intermediate representations for the task at hand. Indeed, differently from commonly used approaches that consider a neural network as a single computational block, i.e., using the output of the last layer only, MOCCA explicitly leverages the multilayer structure of deep architectures. Each layer's feature space is optimized for AD during training, while in the test phase, the deep representations extracted from the trained layers are combined to detect anomalies. With MOCCA, we split the training process into two steps. First, the autoencoder is trained on the reconstruction task only. Then, we only retain the encoder tasked with minimizing the L-2 distance between the output representation and a reference point, the anomaly-free training data centroid, at each considered layer. Subsequently, we combine the deep features extracted at the various trained layers of the encoder model to detect anomalies at inference time. To assess the performance of the models trained with MOCCA, we conduct extensive experiments on publicly available datasets, namely CIFAR10, MVTec AD, and ShanghaiTech. We show that our proposed method reaches comparable or superior performance to state-of-the-art approaches available in the literature. Finally, we provide a model analysis to give insights regarding the benefits of our training procedure.Source: IEEE Transactions on Neural Networks and Learning Systems 33 (2021): 2313–2323. doi:10.1109/TNNLS.2021.3130074
DOI: 10.1109/tnnls.2021.3130074
DOI: 10.48550/arxiv.2012.12111
Project(s): AI4EU
Metrics:

See at: arXiv.org e-Print Archive | IEEE Transactions on Neural Networks and Learning Systems | ISTI Repository | IEEE Transactions on Neural Networks and Learning Systems | doi.org | ieeexplore.ieee.org | IEEE Transactions on Neural Networks and Learning Systems | CNR ExploRA

2020 Conference article Open Access

See at: ISTI Repository | dl.acm.org | dl.acm.org | CNR ExploRA

2020 Journal article Open Access

Cross-resolution learning for face recognition
Massoli F. V., Amato G., Falchi F.
Convolutional Neural Network models have reached extremely high performance on the Face Recognition task. Mostly used datasets, such as VGGFace2, focus on gender, pose, and age variations, in the attempt of balancing them to empower models to better generalize to unseen data. Nevertheless, image resolution variability is not usually discussed, which may lead to a resizing of 256 pixels. While specific datasets for very low-resolution faces have been proposed, less attention has been paid on the task of cross-resolution matching. Hence, the discrimination power of a neural network might seriously degrade in such a scenario. Surveillance systems and forensic applications are particularly susceptible to this problem since, in these cases, it is common that a low-resolution query has to be matched against higher-resolution galleries. Although it is always possible to either increase the resolution of the query image or to reduce the size of the gallery (less frequently), to the best of our knowledge, extensive experimentation of cross-resolution matching was missing in the recent deep learning-based literature. In the context of low- and cross-resolution Face Recognition, the contribution of our work is fourfold: i) we proposed a training procedure to fine-tune a state-of-the-art model to empower it to extract resolution-robust deep features; ii) we conducted an extensive test campaign by using high-resolution datasets (IJB-B and IJB-C) and surveillance-camera-quality datasets (QMUL-SurvFace, TinyFace, and SCface) showing the effectiveness of our algorithm to train a resolution-robust model; iii) even though our main focus was the cross-resolution Face Recognition, by using our training algorithm we also improved upon state-of-the-art model performances considering low-resolution matches; iv) we showed that our approach could be more effective concerning preprocessing faces with super-resolution techniques. The python code of the proposed method will be available at https://github.com/fvmassoli/cross-resolution-face-recognition.Source: Image and vision computing 99 (2020). doi:10.1016/j.imavis.2020.103927
DOI: 10.1016/j.imavis.2020.103927
DOI: 10.48550/arxiv.1912.02851
Project(s): AI4EU
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See at: arXiv.org e-Print Archive | Image and Vision Computing | ISTI Repository | Image and Vision Computing | doi.org | www.sciencedirect.com | CNR ExploRA

Cross-resolution face recognition adversarial attacks
Massoli F. V., Falchi F., Amato G.
Face Recognition is among the best examples of computer vision problems where the supremacy of deep learning techniques compared to standard ones is undeniable. Unfortunately, it has been shown that they are vulnerable to adversarial examples - input images to which a human imperceptible perturbation is added to lead a learning model to output a wrong prediction. Moreover, in applications such as biometric systems and forensics, cross-resolution scenarios are easily met with a non-negligible impact on the recognition performance and adversary's success. Despite the existence of such vulnerabilities set a harsh limit to the spread of deep learning-based face recognition systems to real-world applications, a comprehensive analysis of their behavior when threatened in a cross-resolution setting is missing in the literature. In this context, we posit our study, where we harness several of the strongest adversarial attacks against deep learning-based face recognition systems considering the cross-resolution domain. To craft adversarial instances, we exploit attacks based on three different metrics, i.e., L, L, and L, and we study the resilience of the models across resolutions. We then evaluate the performance of the systems against the face identification protocol, open- and close-set. In our study, we find that the deep representation attacks represents a much dangerous menace to a face recognition system than the ones based on the classification output independently from the used metric. Furthermore, we notice that the input image's resolution has a non-negligible impact on an adversary's success in deceiving a learning model. Finally, by comparing the performance of the threatened networks under analysis, we show how they can benefit from a cross-resolution training approach in terms of resilience to adversarial attacks.Source: Pattern recognition letters 140 (2020): 222–229. doi:10.1016/j.patrec.2020.10.008
DOI: 10.1016/j.patrec.2020.10.008
Project(s): AI4EU
Metrics:

See at: Pattern Recognition Letters | www.sciencedirect.com | CNR ExploRA

2020 Journal article Open Access

Detection of Face Recognition Adversarial Attacks
Massoli F. V., Carrara F., Amato G., Falchi F.
Deep Learning methods have become state-of-the-art for solving tasks such as Face Recognition (FR). Unfortunately, despite their success, it has been pointed out that these learning models are exposed to adversarial inputs - images to which an imperceptible amount of noise for humans is added to maliciously fool a neural network - thus limiting their adoption in sensitive real-world applications. While it is true that an enormous effort has been spent to train robust models against this type of threat, adversarial detection techniques have recently started to draw attention within the scientific community. The advantage of using a detection approach is that it does not require to re-train any model; thus, it can be added to any system. In this context, we present our work on adversarial detection in forensics mainly focused on detecting attacks against FR systems in which the learning model is typically used only as features extractor. Thus, training a more robust classifier might not be enough to counteract the adversarial threats. In this frame, the contribution of our work is four-fold: (i) we test our proposed adversarial detection approach against classification attacks, i.e., adversarial samples crafted to fool an FR neural network acting as a classifier; (ii) using a k-Nearest Neighbor (k-NN) algorithm as a guide, we generate deep features attacks against an FR system based on a neural network acting as features extractor, followed by a similarity-based procedure which returns the query identity; (iii) we use the deep features attacks to fool an FR system on the 1:1 face verification task, and we show their superior effectiveness with respect to classification attacks in evading such type of system; (iv) we use the detectors trained on the classification attacks to detect the deep features attacks, thus showing that such approach is generalizable to different classes of offensives.Source: Computer vision and image understanding (Print) 202 (2020). doi:10.1016/j.cviu.2020.103103
DOI: 10.1016/j.cviu.2020.103103
DOI: 10.48550/arxiv.1912.02918
Project(s): AI4EU
Metrics:

See at: arXiv.org e-Print Archive | Computer Vision and Image Understanding | ISTI Repository | ZENODO | Computer Vision and Image Understanding | doi.org | www.sciencedirect.com | CNR ExploRA

2020 Report Open Access

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2020 Conference article Open Access

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2020 Conference article Open Access

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2020 Report Open Access

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2019 Conference article Open Access

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2019 Conference article Open Access

See at: ISTI Repository | doi.org | link.springer.com | CNR ExploRA