Reviewerly/exHarmony huggingface dataset:
Adversarial Perturbations Fool Deepfake Detectors

This work uses adversarial perturbations to enhance deepfake images and fool common deepfake detectors. We created adversarial perturbations using the Fast Gradient Sign Method and the Carlini and Wagner L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm attack in both blackbox and whitebox settings. Detectors achieved over 95% accuracy on unperturbed deepfakes, but less than 27% accuracy on perturbed deepfakes. We also explore two improvements to deep-fake detectors: (i) Lipschitz regularization, and (ii) Deep Image Prior (DIP). Lipschitz regularization constrains the gradient of the detector with respect to the input in order to increase robustness to input perturbations. The DIP defense removes perturbations using generative convolutional neural networks in an unsupervised manner. Regularization improved the detection of perturbed deepfakes on average, including a 10% accuracy boost in the blackbox case. The DIP defense achieved 95% accuracy on perturbed deepfakes that fooled the original detector while retaining 98% accuracy in other cases on a 100 image subsample.
Towards developing a secure medical image sharing system based on zero trust principles and blockchain technology

Abstract Background Data security has been a critical topic of research and discussion since the onset of data sharing in e-health systems. Although digitalization of data has increased efficiency and speed, it has also made data vulnerable to cyber attacks. Medical records in particular seem to be the regular victims of hackers. Several data breach incidents throughout history have warranted the invention of security measures against these threats. Although various security procedures like firewalls, virtual private networks, encryption, etc are present, a mix of these approaches are required for maximum security in medical image and data sharing. Methods Relatively new, blockchain has become an effective tool for safeguarding sensitive information. However, to ensure overall protection of medical data (images), security measures have to be taken at each step, from the beginning, during and even after transmission of medical images which is ensured by zero trust security model. In this research, a number of studies that deal with these two concepts were studied and a decentralized and trustless framework was proposed by combining these two concepts for secured medical data and image transfer and storage. Results Research output suggested blockchain technology ensures data integrity by maintaining an audit trail of every transaction while zero trust principles make sure the medical data is encrypted and only authenticated users and devices interact with the network. Thus the proposed model solves a lot of vulnerabilities related to data security. Conclusions A system to combat medical/health data vulnerabilities has been proposed. The system makes use of the immutability of blockchain, the additional security of zero trust principles, and the scalability of off chain data storage using Inter Planetary File Systems (IPFS). The adoption of this system suggests to enhance the security of medical or health data transmission.
A three-way density peak clustering method based on evidence theory

Density peaks clustering (DPC) algorithm is an efficient and simple clustering method attracting the attention of many researchers. However, its strategy of assigning each non-grouped object to the same cluster depends on its nearest neighbors having a higher local density. This may lead to the cluster label error propagation problem, i.e. if an object is wrong-labeled during the clustering process, its label will be propagated in the subsequent assignment. To overcome this defect, in this paper we propose a three-way density peak clustering method based on evidence theory, referred to as 3W-DPET. 3W-DPET forms clusters as interval sets using three-way clustering representation including three disjoint regions called positive region (POS), boundary region (BND) and negative region (NEG). 3W-DPET mainly consists of three steps: (1) finding out cluster centers and noises before clustering; (2) using a midrange distance comparison method to detect positive regions of clusters; and (3) allocating the remaining non-grouped objects, including noises, to the boundary region or the negative region of clusters. The distinguishing feature of 3W-DPET is that evidence theory is used to construct and collect the information of K-nearest neighbors in order to assign non-grouped objects to the most suitable cluster, which can effectively solve the problem of cluster label error propagation. In order to validate 3W-DPET, we test it on 18 datasets using three benchmarks (ACC, ARI and NMI), and compare it to K-means, FCM, DPC, KNN-DPC, DPCSA, SNN-DPC and CE3-kmeans methods. Experimental results suggest that 3W-DPET can effectively find clusters and its results conform with human cognition.
Multiple images encryption based on 3D scrambling and hyper-chaotic system

The growth and popularization of network and multimedia technologies have led to an increase in digital data over the internet. Most of the data is confidential and requires an efficient algorithm to securely transmit the data over the insecure channel. The paper proposes a cryptographic algorithm which can be used for securing multiple digital images over the network. The algorithm uses the concept of chaos theory and elliptic curve Elgamal cryptosystem for the generation of the cipher image and sharing of the key. A 3D image is generated using the multiple plain images and undergoes permutation and substitution phase for generation of the cipher data. Experimental results and analysis shows that the proposed algorithm has got large keyspace, desired statistical properties of cipher data and resistant to attacks.
Adoption of artificial intelligence integrated CRM system: an empirical study of Indian organizations

Purpose The purpose of this study is to explore the behavioral intention of the employees to adopt artificial intelligence (AI) integrated customer relationship management (CRM) system in Indian organizations. Design/methodology/approach To identify the factors impacting the behavioral intention of the employees to adopt AI integrated CRM system in Indian organizations helps of literature review and theories have been taken. Thereafter, some hypotheses have been formulated followed by the development of a theoretical model conceptually. The model has been tested statistically for validation using a survey by considering 308 usable respondents. Findings The results of this study show that perceived usefulness and perceived ease of use directly impact the behavioral intention of the employees to adopt an AI integrated CRM system in organizations. Also, these two exogenous factors impact the behavioral intention of the employees to adopt an AI integrated CRM system mediating through two intermediate variables such as utilitarian attitude (UTA) and hedonic attitude (HEA). The proposed model has achieved predictive power of 67%. Research limitations/implications By the help of the technology acceptance model and motivational theory, the predictors of behavioral intention to adopt AI integrated CRM systems in organizations were identified. The effectiveness of the model was strengthened by the consideration of two employee-centric attitudinal attributes such as UTA and HEA, which is claimed to have provided contributions to the extant literature. The proposed theoretical model claims a special theoretical contribution as no extant literature considered the effects of leadership support as a moderator for the adoption of an AI integrated CRM system in Indian organizations. Practical implications The model implies that the employees using AI integrated CRM system in organizations must be made aware of the usefulness of the system and the employees must not face any complexity to use the system. For this, the managers of the concerned organizations must create a conducive atmosphere congenial for the employees to use the AI integrated CRM system in the organizations. Originality/value Studies covering exploration of the adoption of AI integrated CRM systems in Indian organizations are found to be in a rudimentary stage and in that respect, this study claims to have possessed its uniqueness.
Attribute-Based Access Control for Smart Cities: A Smart-Contract-Driven Framework

Efficient and reliable access control in smart cities is critical for the protection of various resources for decision making and task execution. Existing centralized access control schemes suffer from the limitations of single point of failure, low reliability, and poor scalability. This article, therefore, proposes a distributed and reliable access control framework for smart cities by combining the blockchain smart contract technology and the attribute-based access control (ABAC) model. The framework consists of one policy management contract (PMC) for managing the ABAC policies, one subject attribute management contract (SAMC) for managing the attributes of subjects (i.e., entities accessing resources), one object attribute management contract (OAMC) for managing the attributes of objects (i.e., resources being accessed), and one access control contract (ACC) for performing the access control. To show the feasibility of the proposed framework, we construct a local private Ethereum blockchain system to implement the four smart contracts and also conduct experiments to evaluate the monetary cost as well as to compare the proposed framework with an existing access control list (ACL)-based scheme. The experimental results show that although the proposed scheme consumes more money than the ACL-based scheme at the deployment stage, it introduces less monetary cost during the system running especially for large-scale smart cities.
Survey on graph embeddings and their applications to machine learning problems on graphs

Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.
Jointly De-Biasing Face Recognition and Demographic Attribute Estimation

We address the problem of bias in automated face recognition and demographic attribute estimation algorithms, where errors are lower on certain cohorts belonging to specific demographic groups. We present a novel de-biasing adversarial network (DebFace) that learns to extract disentangled feature representations for both unbiased face recognition and demographics estimation. The proposed network consists of one identity classifier and three demographic classifiers (for gender, age, and race) that are trained to distinguish identity and demographic attributes, respectively. Adversarial learning is adopted to minimize correlation among feature factors so as to abate bias influence from other factors. We also design a new scheme to combine demographics with identity features to strengthen robustness of face representation in different demographic groups. The experimental results show that our approach is able to reduce bias in face recognition as well as demographics estimation while achieving state-of-the-art performance.
ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations

The pre-training of text encoders normally processes text as a sequence of tokens corresponding to small text units, such as word pieces in English and characters in Chinese.It omits information carried by larger text granularity, and thus the encoders cannot easily adapt to certain combinations of characters.This leads to a loss of important semantic information, which is especially problematic for Chinese because the language does not have explicit word boundaries.In this paper, we propose ZEN, a BERT-based Chinese (Z) text encoder Enhanced by N-gram representations, where different combinations of characters are considered during training, thus potential word or phrase boundaries are explicitly pre-trained and fine-tuned with the character encoder (BERT).Therefore ZEN incorporates the comprehensive information of both the character sequence and words or phrases it contains.Experimental results illustrated the effectiveness of ZEN on a series of Chinese NLP tasks, where state-of-the-art results is achieved on most tasks with requiring less resource than other published encoders.It is also shown that reasonable performance is obtained when ZEN is trained on a small corpus, which is important for applying pre-training techniques to scenarios with limited data.
DeepCorrect: Correcting DNN Models Against Image Distortions

In recent years, the widespread use of deep neural networks (DNNs) has facilitated great improvements in performance for computer vision tasks like image classification and object recognition. In most realistic computer vision applications, an input image undergoes some form of image distortion such as blur and additive noise during image acquisition or transmission. Deep networks trained on pristine images perform poorly when tested on such distortions. In this paper, we evaluate the effect of image distortions like Gaussian blur and additive noise on the activations of pre-trained convolutional filters. We propose a metric to identify the most noise susceptible convolutional filters and rank them in order of the highest gain in classification accuracy upon correction. In our proposed approach called DeepCorrect, we apply small stacks of convolutional layers with residual connections at the output of these ranked filters and train them to correct the worst distortion affected filter activations, while leaving the rest of the pre-trained filter outputs in the network unchanged. Performance results show that applying DeepCorrect models for common vision tasks like image classification (ImageNet), object recognition (Caltech-101, Caltech-256), and scene classification (SUN-397), significantly improves the robustness of DNNs against distorted images and outperforms other alternative approaches.
Rethinking Class Activation Mapping for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) is a task of localizing an object in an image only using image-level labels. To tackle the WSOL problem, most previous studies have followed the conventional class activation mapping (CAM) pipeline: (i) training CNNs for a classification objective, (ii) generating a class activation map via global average pooling (GAP) on feature maps, and (iii) extracting bounding boxes by thresholding based on the maximum value of the class activation map. In this work, we reveal the current CAM approach suffers from three fundamental issues: (i) the bias of GAP that assigns a higher weight to a channel with a small activation area, (ii) negatively weighted activations inside the object regions and (iii) instability from the use of the maximum value of a class activation map as a thresholding reference. They collectively cause the problem that the localization to be highly limited to small regions of an object. We propose three simple but robust techniques that alleviate the problems, including thresholded average pooling, negative weight clamping, and percentile as a standard for thresholding. Our solutions are universally applicable to any WSOL methods using CAM and improve their performance drastically. As a result, we achieve the new state-of-the-art performance on three benchmark datasets of CUB-200–2011, ImageNet-1K, and OpenImages30K.
Generalizing Person Re-Identification by Camera-Aware Invariance Learning and Cross-Domain Mixup

Despite the impressive performance under the single-domain setup, current fully-supervised models for person re-identification (re-ID) degrade significantly when deployed to an unseen domain. According to the characteristics of cross-domain re-ID, such degradation is mainly attributed to the dramatic variation within the target domain and the severe shift between the source and target domain. To achieve a model that generalizes well to the target domain, it is desirable to take both issues into account. In terms of the former issue, one of the most successful solutions is to enforce consistency between nearest-neighbors in the embedding space. However, we find that the search of neighbors is highly biased due to the discrepancy across cameras. To this end, we improve the vanilla neighborhood invariance approach by imposing the constraint in a camera-aware manner. As for the latter issue, we propose a novel cross-domain mixup scheme. It alleviates the abrupt transfer by introducing the interpolation between the two domains as a transition state. Extensive experiments on three public benchmarks demonstrate the superiority of our method. Without any auxiliary data or models, it outperforms existing state-of-the-arts by a large margin. The code is available at https://github.com/LuckyDC/generalizing-reid .
Phase Gadget Synthesis for Shallow Circuits

We give an overview of the circuit optimisation methods used by tket, a compiler system for quantum software developed by Cambridge Quantum Computing Ltd. We focus on a novel technique based around phase gadgets, a family of multi-qubit quantum operations which occur naturally in a wide range of quantum circuits of practical interest. The phase gadgets have a simple presentation in the ZX-calculus, which makes it easy to reason about them. Taking advantage of this, we present an efficient method to translate the phase gadgets back to CNOT gates and single qubit operations suitable for execution on a quantum computer with significant reductions in gate count and circuit depth. We demonstrate the effectiveness of these methods on a quantum chemistry benchmarking set based on variational circuits for ground state estimation of small molecules.
Topology-Preserving Class-Incremental Learning

A well-known issue for class-incremental learning is the catastrophic forgetting phenomenon, where the network’s recognition performance on old classes degrades severely when incrementally learning new classes. To alleviate forgetting, we put forward to preserve the old class knowledge by maintaining the topology of the network’s feature space. On this basis, we propose a novel topology-preserving class-incremental learning (TPCIL) framework. TPCIL uses an elastic Hebbian graph (EHG) to model the feature space topology, which is constructed with the competitive Hebbian learning rule. To maintain the topology, we develop the topology-preserving loss (TPL) that penalizes the changes of EHG’s neighboring relationships during incremental learning phases. Comprehensive experiments on CIFAR100, ImageNet, and subImageNet datasets demonstrate the power of the TPCIL for continuously learning new classes with less forgetting. The code will be released.
UnionDet: Union-Level Detector Towards Real-Time Human-Object Interaction Detection

Recent advances in deep neural networks have achieved significant progress in detecting individual objects from an image. However, object detection is not sufficient to fully understand a visual scene. Towards a deeper visual understanding, the interactions between objects, especially humans and objects are essential. Most prior works have obtained this information with a bottom-up approach, where the objects are first detected and the interactions are predicted sequentially by pairing the objects. This is a major bottleneck in HOI detection inference time. To tackle this problem, we propose UnionDet, a one-stage meta-architecture for HOI detection powered by a novel union-level detector that eliminates this additional inference stage by directly capturing the region of interaction. Our one-stage detector for human-object interaction shows a significant reduction in interaction prediction time ( $$4{\times }{\sim }14{\times }$$ ) while outperforming state-of-the-art methods on two public datasets: V-COCO and HICO-DET.
Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flows

Monocular 3D human pose and shape estimation is challenging due to the many degrees of freedom of the human body and the difficulty to acquire training data for large-scale supervised learning in complex visual scenes. In this paper we present practical semi-supervised and self-supervised models that support training and good generalization in real-world images and video. Our formulation is based on kinematic latent normalizing flow representations and dynamics, as well as differentiable, semantic body part alignment loss functions that support self-supervised learning. In extensive experiments using 3D motion capture datasets like CMU, Human3.6M, 3DPW, or AMASS, as well as image repositories like COCO, we show that the proposed methods outperform the state of the art, supporting the practical construction of an accurate family of models based on large-scale training with diverse and incompletely labeled image and video data.
Post-training Piecewise Linear Quantization for Deep Neural Networks

Quantization plays an important role in the energy-efficient deployment of deep neural networks on resource-limited devices. Post-training quantization is highly desirable since it does not require retraining or access to the full training dataset. The well-established uniform scheme for post-training quantization achieves satisfactory results by converting neural networks from full-precision to 8-bit fixed-point integers. However, it suffers from significant performance degradation when quantizing to lower bit-widths. In this paper, we propose a piecewise linear quantization (PWLQ) scheme (Code will be made available at https://github.com/jun-fang/PWLQ ) to enable accurate approximation for tensor values that have bell-shaped distributions with long tails. Our approach breaks the entire quantization range into non-overlapping regions for each tensor, with each region being assigned an equal number of quantization levels. Optimal breakpoints that divide the entire range are found by minimizing the quantization error. Compared to state-of-the-art post-training quantization methods, experimental results show that our proposed method achieves superior performance on image classification, semantic segmentation, and object detection with minor overhead.