Reviewerly/exHarmony huggingface dataset:
TI<sup>2</sup>Net: Temporal Identity Inconsistency Network for Deepfake Detection

In this paper, we propose the Temporal Identity Inconsistency Network (TI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net), a Deepfake detector that focuses on temporal identity inconsistency. Specifically, TI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net recognizes fake videos by capturing the dissimilarities of human faces among video frames of the same identity. Therefore, TI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net is a reference-agnostic detector and can be used on unseen datasets. For a video clip of a given identity, identity information in all frames will first be encoded to identity vectors. TI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net learns the temporal identity embedding from the temporal difference of the identity vectors. The temporal embedding, representing the identity inconsistency in the video clip, is finally used to determine the authenticity of the video clip. During training, TI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net incorporates triplet loss to learn more discriminative temporal embeddings. We conduct comprehensive experiments to evaluate the performance of the proposed TI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net. Experimental results indicate that TI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net generalizes well to unseen manipulations and datasets with unseen identities. Besides, TI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net also shows robust performance against compression and additive noise.
Reliable Detection of Location Spoofing and Variation Attacks

Location spoofing is a critical attack in mobile communications. While several previous studies investigated the detection of location spoofing attacks, they are limited in their performance and lack the consideration of emerging attack variations. In this paper, we present a data-driven methodology for the reliable detection of location spoofing and its variations. To enhance the performance, we introduce and utilize a new set of features, which is differential in nature and enables the checking of the mobility constraints and inconsistency. Our comparison study with the previous research shows that the presented scheme using the new features significantly improves the accuracy and reliability of the detection against location spoofing attacks. To take the possibility of attack variations into account, we establish a set of scenarios manipulating coordinate data to create attack variants. Our experimental results confirm the feasibility and effectiveness of the new features for identifying diverse types of spoofing attacks and their variations, greatly improving the detection performance by up to 99.1% accuracy. Additionally, we present a profiling-based detection approach (building the detector referring only to legitimate coordinate data), to further extend resilience to previously unseen attacks as a means to zero-day detection. The evaluation result shows the potential of the profiling-based detector with comparable or even better performance than the supervised learning methods (requiring both legitimate and falsified data to construct the detector).
Realistic Full-Body Anonymization with Surface-Guided GANs

Recent work on image anonymization has shown that generative adversarial networks (GANs) can generate near-photorealistic faces to anonymize individuals. However, scaling up these networks to the entire human body has remained a challenging and yet unsolved task. We propose a new anonymization method that generates realistic humans for in-the-wild images. A key part of our design is to guide adversarial nets by dense pixel-to-surface correspondences between an image and a canonical 3D surface. We introduce Variational Surface-Adaptive Modulation (V-SAM) that embeds surface information throughout the generator. Combining this with our novel discriminator surface supervision loss, the generator can synthesize high quality humans with diverse appearances in complex and varying scenes. We demonstrate that surface guidance significantly improves image quality and diversity of samples, yielding a highly practical generator. Finally, we show that our method preserves data usability without infringing privacy when collecting image datasets for training computer vision models. Source code and appendix is available at: github.com/hukkelas/full_body_anonymization
Dynamic Mixture of Counter Network for Location-Agnostic Crowd Counting

Crowd counting has attracted increasing attentions in recent years due to its challenges and wide societal applications. Despite persevering efforts made by the research community, most of existing methods require a large amount of location-level annotations. Collecting such type of fine-granularity supervisory signals is extremely time-consuming and labour-intensive, thereby hindering the well generalization of these location-adherent models. To shun this drawback, several pioneering studies open a promising research direction of location-agonistic crowd counting. Albeit the noticeable efforts, they somewhat ignore the merits of diverse learning paradigms and the issue of intractable density shift. To ameliorate these issues, in this paper, a novel Dynamic Mixture of Counter Network (DMCNet) is proposed for location-agnostic crowd counting. Specifically, our DMCNet inherits the hybrid advantages of CNNs (e.g. locality-oriented and pyramidal property) and MLP-based structure (e.g. global receptive fields and light weight). Particularly, the dynamic counter predictor and the mixture of counter heads are delicately designed to hammer at combating huge density shift and overfitting. Extensive experiments demonstrate that our DMCNet attains state-of-the-art performance against existing location-agnostic approaches and performs on par with many conventional location-adherent ones.
PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch

Adversarial patch attacks mislead neural networks by injecting adversarial pixels within a local region. Patch attacks can be highly effective in a variety of tasks and physically realizable via attachment (e.g. a sticker) to the real-world objects. Despite the diversity in attack patterns, adversarial patches tend to be highly textured and different in appearance from natural images. We exploit this property and present PatchZero, a general defense pipeline against white-box adversarial patches without retraining the downstream classifier or detector. Specifically, our defense detects adversaries at the pixel-level and "zeros out" the patch region by repainting with mean pixel values. We further design a two-stage adversarial training scheme to defend against the stronger adaptive attacks. PatchZero achieves SOTA defense performance on the image classification (ImageNet, RESISC45), object detection (PASCAL VOC), and video classification (UCF101) tasks with little degradation in benign performance. In addition, PatchZero transfers to different patch shapes and attack types.
Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats

Deep learning-based 3D human pose estimation performs best when trained on large amounts of labeled data, making combined learning from many datasets an important research direction. One obstacle to this endeavor are the different skeleton formats provided by different datasets, i.e., they do not label the same set of anatomical landmarks. There is little prior research on how to best supervise one model with such discrepant labels. We show that simply using separate output heads for different skeletons results in inconsistent depth estimates and insufficient information sharing across skeletons. As a remedy, we propose a novel affine-combining autoencoder (ACAE) method to perform dimensionality reduction on the number of landmarks. The discovered latent 3D points capture the redundancy among skeletons, enabling enhanced information sharing when used for consistency regularization. Our approach scales to an extreme multi-dataset regime, where we use 28 3D human pose datasets to supervise one model, which outperforms prior work on a range of benchmarks, including the challenging 3D Poses in the Wild (3DPW) dataset. Our code and models are available for research purposes. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
TVCalib: Camera Calibration for Sports Field Registration in Soccer

Sports field registration in broadcast videos is typically interpreted as the task of homography estimation, which provides a mapping between a planar field and the corresponding visible area of the image. In contrast to previous approaches, we consider the task as a camera calibration problem. First, we introduce a differentiable objective function that is able to learn the camera pose and focal length from segment correspondences (e.g., lines, point clouds), based on pixel-level annotations for segments of a known calibration object. The calibration module iteratively minimizes the segment reprojection error induced by the estimated camera parameters. Second, we propose a novel approach for 3D sports field registration from broadcast soccer images. Compared to the typical solution, which subsequently refines an initial estimation, our solution does it in one step. The proposed method is evaluated for sports field registration on two datasets and achieves superior results compared to two state-of-the-art approaches.
Large-scale agricultural investment and female employment in African communities: Quantitative and qualitative insights from Nigeria

This study examines how large-scale agricultural investments (LSAIs) affect employment outcomes of female-headed households in Nigeria. It focuses on wage income and labour allocations of households in communities where LSAIs occurred in comparison with households in communities where LSAIs did not occur. It engages a mixed method approach, which involves the quantitative and qualitative. The quantitative data was sourced from the Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), which was analysed using Propensity Score Matching (PSM). The qualitative analysis entails in-depth interviews (IDIs) and focus group discussions (FGDs). The results show a positive relationship with the household income living in communities with LSAIs, but a negative association with labour allocation to agricultural activities. Also, the findings indicate that households in communities where LSAIs took place received higher wages and spent fewer hours in agriculture. Though, female-headed households spent more time on agricultural activities than the male-headed households, they earn less. The analysis from the qualitative study show, among others things, that female-headed households spent more time on off-farm business despite the fact that they earn less. The study concludes by recommending that the possible adverse employment effects of LSAIs could be reduced by optimising its positive impact, especially with respect to female-headed households in rural communities where most of such investments occur.
Secure PIN-Entry Method Using One-Time PIN (OTP)

The regular PIN-entry method has been still the most common method of authentication for systems and networks. However, PINs are easy to be captured through various attacks, including shoulder-surfing, video-recording, and spyware. This could be attributed to the involuntary nature of entering the original PIN during authentication. In this paper, we employ an indirect input method that utilizes the addition mod 10 and a mini-challenge keypad in order to produce a one-time PIN (OTP) that obscures the original PIN. The results of our user study manifest that the proposed PIN-entry method provides better security than the existing PIN-entry methods while maintaining an acceptable level of usability. Moreover, the user feedback fully support the use of the proposed PIN-entry method in critical-security situations.
FS-IBEKS: Forward secure identity-based encryption with keyword search from lattice

Public Key Encryption with Keyword Search (PEKS) makes it possible for a cloud server (CS) to match a trapdoor and a ciphertext. However, with the upgrowth of quantum techniques, most of the existing PEKS schemes will be broken by quantum computers in the coming future. Moreover, they are also under the threat of potential key exposure. Lattice-based forward secure PEKS scheme (FS-PEKS) overcomes the two problems above by combining the techniques of forward security and lattice-based cryptography. However, FS-PEKS schemes work in public key infrastructure (PKI), which will incur complicated certificate management procedures. In this work, to overcome the key management issue but still guarantee security even when attackers corrupt the keys, we extend the FS-PEKS scheme into the identity-based framework and present a forward secure identity-based encryption with keyword search (FS-IBEKS) scheme from lattice. The proposed scheme is secured under the selective identity against chosen plaintext attack (IND-sID-CPA) in the random oracle model. To further improve the security, we present another FS-IBEKS scheme into the standard model and give concrete security proof under the adaptive identity against chosen plaintext attack (IND-ID-CPA). The comprehensive performance evaluation demonstrates that our FS-IBEKS schemes are feasible for cloud computing.
A Blockchain-Based Framework Gamification for Securing Learners Activity in Merdeka Belajar-Kampus Merdeka

In the life of Education 4.0, information is very vulnerable to security. Assessment is an integral part of learning. In addition to attendance, assignments are the main point in determining the graduation of a course. However, there are some safeguards for student activity assessment input platforms, which are dangerous, and data leaks can occur. Taking advantage of its open-source, transparent, and immutable or immutable nature can help secure the various platforms used. The University's need for various information makes the application of Blockchain technology a very appropriate thing to use. With the use of Blockchain, the security of student activities is more guaranteed, and it is easier to control by the Lecturer Board. This study aims to secure student activities from forgery and fraud when inputting activities using blockchain technology. The framework created also uses gamification as something that motivates learning activities. The author has analyzed the security needed for activity verification in the form of levels on several applications, one of which is a platform-based application used for the Merdeka Learning-Independence Campus platform. The method used in this research is descriptive research in the form of a literature study that prioritizes gamification in a platform framework. The urgency of this research is to secure the Merdeka Learning-Independence Campus platform for students who need more security in their data storage. If security is increased, student confidence and learning motivation towards the activities carried out will increase so that learning motivation will be better so that the quality of learning will be better.
Quantum error mitigation by Pauli check sandwiching

We describe and analyze an error mitigation technique that uses multiple pairs of parity checks to detect the presence of errors. Each pair of checks uses one ancilla qubit to detect a component of the error operator and represents one layer of the technique. We build on the results on extended flag gadgets and put it on a firm theoretical foundation. We prove that this technique can recover the noiseless state under the assumption of noise not affecting the checks. The method does not incur any encoding overhead and instead chooses the checks based on the input circuit. We provide an algorithm for obtaining such checks for an arbitrary target circuit. Since the method applies to any circuit and input state, it can be easily combined with other error mitigation techniques. We evaluate the performance of the proposed methods using extensive numerical simulations on 1850 random input circuits composed of Clifford gates and non-Clifford single-qubit rotations, a class of circuits encompassing most commonly considered variational algorithm circuits. We observe average improvements in fidelity of 34 percentage points with six layers of checks.
Building Trusted Federated Learning: Key Technologies and Challenges

Federated learning (FL) provides convenience for cross-domain machine learning applications and has been widely studied. However, the original FL is still vulnerable to poisoning and inference attacks, which will hinder the landing application of FL. Therefore, it is essential to design a trustworthy federation learning (TFL) to eliminate users’ anxiety. In this paper, we aim to provide a well-researched picture of the security and privacy issues in FL that can bridge the gap to TFL. Firstly, we define the desired goals and critical requirements of TFL, observe the FL model from the perspective of the adversaries and extrapolate the roles and capabilities of potential adversaries backward. Subsequently, we summarize the current mainstream attack and defense means and analyze the characteristics of the different methods. Based on a priori knowledge, we propose directions for realizing the future of TFL that deserve attention.
Edge-aided searchable data sharing scheme for IoV in the 5G environment

The development of 5G technology provides huge potential for the Internet of Vehicles (IoV). In the IoV scenario, the secure exchange of traffic warning data is necessary to guarantee the safety of pedestrians, vehicles, and public transport facilities. However, there remain a series of challenging issues to achieve data secure sharing while ensuring efficiency. Current solutions such as the data outsourcing storage of traditional centralized cloud servers may cause information leakage due to their incomplete credibility. To address these problems mentioned above, this paper proposes an edge-aided searchable data sharing scheme based on blockchain for IoV in a 5G environment. First, the proposed scheme stores the search index of the keyword and the hash value of the data ciphertext on the blockchain to ensure its integrity and authenticity, and the smart contract automatically verifies the search trapdoor. And then, an edge caching mechanism is designed to quickly feedback on users' retrieval needs based on a cloud-edge collaboration model. In addition, attribute-based searchable encryption is used to ensure that only the users who meet the access attributes and policy can decrypt the encrypted traffic warning data, which can achieve fine-grained access control and keyword search simultaneously. Finally, security analysis and performance evaluation demonstrate that our scheme is secure and more efficient compared with other schemes.
Energy Consumption Analysis of the Selected Navigation Algorithms for Wheeled Mobile Robots

The article presents the research on navigation algorithms of a wheeled mobile robot with the use of a vision mapping system and the analysis of energy consumption of selected navigation algorithms, such as RRT and A-star. Obstacle maps were made with the use of an RGBW camera, and binary occupation maps were also made, which were used to determine the traffic path. To recreate the routes in hardware, a programmed Pure Pursuit controller was used. The results of navigation were compared on the basis of the forward kinematics model and odometry measurements. Quantities such as current, except (x, y, phi), and linear and angular velocities were measured in real time. As a result of the conducted research, it was found that the RRT star algorithm consumes the least energy to reach the designated target in the designated environment.
Self-attention random forest for breast cancer image classification

Introduction Early screening and diagnosis of breast cancer can not only detect hidden diseases in time, but also effectively improve the survival rate of patients. Therefore, the accurate classification of breast cancer images becomes the key to auxiliary diagnosis. Methods In this paper, on the basis of extracting multi-scale fusion features of breast cancer images using pyramid gray level co-occurrence matrix, we present a Self-Attention Random Forest (SARF) model as a classifier to explain the importance of fusion features, and can perform adaptive refinement processing on features, thus, the classification accuracy can be improved. In addition, we use GridSearchCV technique to optimize the hyperparameters of the model, which greatly avoids the limitation of artificially selected parameters. Results To demonstrate the effectiveness of our method, we perform validation on the breast cancer histopathological image-BreaKHis. The proposed method achieves an average accuracy of 92.96% and a micro average AUC value of 0.9588 for eight-class classification, and an average accuracy of 97.16% and an AUC value of 0.9713 for binary classification on BreaKHis dataset. Discussion For the sake of verify the universality of the proposed model, we also conduct experiments on MIAS dataset. An excellent average classification accuracy is 98.79% on MIAS dataset. Compared to other state-of-the-art methods, the experimental results demonstrate that the performance of the proposed method is superior to that of others. Furthermore, we can analyze the influence of different types of features on the proposed model, and provide theoretical basis for further optimization of the model in the future.
DeReFNet: Dual-stream Dense Residual Fusion Network for static hand gesture recognition

Vision-based hand gesture recognition (HGR) system provides the most effective and natural way of interaction between humans and machines. However, the recognition performance of such an HGR system is challenging due to the variations in illumination, complex backgrounds, the shape of the user's hand, and inter-class similarity. This work proposes a compact dual-stream dense residual fusion network (DeReFNet) to address the above challenges. The proposed convolutional neural network architecture mainly utilizes the strength of global features from each residual block of the residual stream and spatial information from the other stream using dense connectivity. Both the streams are fused to gather enriched information using the feature concatenation module. The efficacy of the DeReFNet is validated using a subject-independent cross-validation technique on four publicly available benchmark datasets. Furthermore, the qualitative and quantitative analysis of the benchmarked datasets illustrates that the DeReFNet outperforms state-of-the-art methods in terms of accuracy and computational time.