BENet: A Cross-domain Robust Network for Detecting Face Forgeries via Bias Expansion and Latent-space Attention

In response to the growing threat of deepfake technology, we introduce BENet, a Cross-Domain Robust Bias Expansion Network. BENet enhances the detection of fake faces by addressing limitations in current detectors related to variations across different types of fake face generation techniques, where ``cross-domain" refers to the diverse range of these deepfakes, each considered a separate domain. BENet's core feature is a bias expansion module based on autoencoders. This module maintains genuine facial features while enhancing differences in fake reconstructions, creating a reliable bias for detecting fake faces across various deepfake domains. We also introduce a Latent-Space Attention (LSA) module to capture inconsistencies related to fake faces at different scales, ensuring robust defense against advanced deepfake techniques. The enriched LSA feature maps are multiplied with the expanded bias to create a versatile feature space optimized for subtle forgeries detection. To improve its ability to detect fake faces from unknown sources, BENet integrates a cross-domain detector module that enhances recognition accuracy by verifying the facial domain during inference. We train our network end-to-end with a novel bias expansion loss, adopted for the first time, in face forgery detection. Extensive experiments covering both intra and cross-dataset demonstrate BENet's superiority over current state-of-the-art solutions.

Support Vector Machine for Person Classification Using the EEG Signals

User authentication is a pivotal element in security systems. Conventional methods including passwords, personal identification numbers, and identification tags are increasingly vulnerable to cyber-attacks. This paper suggests a paradigm shift towards biometric identification technology that leverages unique physiological or behavioral characteristics for user authenticity verification. Nevertheless, biometric solutions like fingerprints, iris patterns, facial and voice recognition are also susceptible to forgery and deception. We propose using Electroencephalogram (EEG) signals for individual identification to address this challenge. Derived from unique brain activities, these signals offer promising authentication potential and provide a novel means for liveness detection, thereby mitigating spoofing attacks. This study employs a public dataset initially compiled for fatigue analysis, featuring EEG data from 12 subjects recorded via an eight-channel OpenBCI helmet. This dataset extracts salient features from the EEG signals and trains a supervised multiclass Support Vector Machine classifier. Upon evaluation, the classifier model achieves a maximum accuracy of 92.9\%, leveraging ten features from each channel. Collectively, these findings highlight the viability of machine learning in implementing real-world, EEG-based biometric identification systems, thereby advancing user authentication technology.

Classification in Japanese Sign Language Based on Dynamic Facial Expressions

Sign language is a visual language expressed through hand movements and non-manual markers. Non-manual markers include facial expressions and head movements. These expressions vary across different nations. Therefore, specialized analysis methods for each sign language are necessary. However, research on Japanese Sign Language (JSL) recognition is limited due to a lack of datasets. The development of recognition models that consider both manual and non-manual features of JSL is crucial for precise and smooth communication with deaf individuals. In JSL, sentence types such as affirmative statements and questions are distinguished by facial expressions. In this paper, we propose a JSL recognition method that focuses on facial expressions. Our proposed method utilizes a neural network to analyze facial features and classify sentence types. Through the experiments, we confirm our method's effectiveness by achieving a classification accuracy of 96.05%.

OSDFace: One-Step Diffusion Model for Face Restoration

Diffusion models have demonstrated impressive performance in face restoration. Yet, their multi-step inference process remains computationally intensive, limiting their applicability in real-world scenarios. Moreover, existing methods often struggle to generate face images that are harmonious, realistic, and consistent with the subject's identity. In this work, we propose OSDFace, a novel one-step diffusion model for face restoration. Specifically, we propose a visual representation embedder (VRE) to better capture prior information and understand the input face. In VRE, low-quality faces are processed by a visual tokenizer and subsequently embedded with a vector-quantized dictionary to generate visual prompts. Additionally, we incorporate a facial identity loss derived from face recognition to further ensure identity consistency. We further employ a generative adversarial network (GAN) as a guidance model to encourage distribution alignment between the restored face and the ground truth. Experimental results demonstrate that OSDFace surpasses current state-of-the-art (SOTA) methods in both visual quality and quantitative metrics, generating high-fidelity, natural face images with high identity consistency. The code and model will be released at https://github.com/jkwang28/OSDFace.

ErasableMask: A Robust and Erasable Privacy Protection Scheme against Black-box Face Recognition Models

While face recognition (FR) models have brought remarkable convenience in face verification and identification, they also pose substantial privacy risks to the public. Existing facial privacy protection schemes usually adopt adversarial examples to disrupt face verification of FR models. However, these schemes often suffer from weak transferability against black-box FR models and permanently damage the identifiable information that cannot fulfill the requirements of authorized operations such as forensics and authentication. To address these limitations, we propose ErasableMask, a robust and erasable privacy protection scheme against black-box FR models. Specifically, via rethinking the inherent relationship between surrogate FR models, ErasableMask introduces a novel meta-auxiliary attack, which boosts black-box transferability by learning more general features in a stable and balancing optimization strategy. It also offers a perturbation erasion mechanism that supports the erasion of semantic perturbations in protected face without degrading image quality. To further improve performance, ErasableMask employs a curriculum learning strategy to mitigate optimization conflicts between adversarial attack and perturbation erasion. Extensive experiments on the CelebA-HQ and FFHQ datasets demonstrate that ErasableMask achieves the state-of-the-art performance in transferability, achieving over 72% confidence on average in commercial FR systems. Moreover, ErasableMask also exhibits outstanding perturbation erasion performance, achieving over 90% erasion success rate.

Local and Global Feature Attention Fusion Network for Face Recognition

Recognition of low-quality face images remains a challenge due to invisible or deformation in partial facial regions. For low-quality images dominated by missing partial facial regions, local region similarity contributes more to face recognition (FR). Conversely, in cases dominated by local face deformation, excessive attention to local regions may lead to misjudgments, while global features exhibit better robustness. However, most of the existing FR methods neglect the bias in feature quality of low-quality images introduced by different factors. To address this issue, we propose a Local and Global Feature Attention Fusion (LGAF) network based on feature quality. The network adaptively allocates attention between local and global features according to feature quality and obtains more discriminative and high-quality face features through local and global information complementarity. In addition, to effectively obtain fine-grained information at various scales and increase the separability of facial features in high-dimensional space, we introduce a Multi-Head Multi-Scale Local Feature Extraction (MHMS) module. Experimental results demonstrate that the LGAF achieves the best average performance on $4$ validation sets (CFP-FP, CPLFW, AgeDB, and CALFW), and the performance on TinyFace and SCFace outperforms the state-of-the-art methods (SoTA).

Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models

This study explores the use of Generative Adversarial Networks (GANs) to detect AI deepfakes and fraudulent activities in online payment systems. With the growing prevalence of deepfake technology, which can manipulate facial features in images and videos, the potential for fraud in online transactions has escalated. Traditional security systems struggle to identify these sophisticated forms of fraud. This research proposes a novel GAN-based model that enhances online payment security by identifying subtle manipulations in payment images. The model is trained on a dataset consisting of real-world online payment images and deepfake images generated using advanced GAN architectures, such as StyleGAN and DeepFake. The results demonstrate that the proposed model can accurately distinguish between legitimate transactions and deepfakes, achieving a high detection rate above 95%. This approach significantly improves the robustness of payment systems against AI-driven fraud. The paper contributes to the growing field of digital security, offering insights into the application of GANs for fraud detection in financial services. Keywords- Payment Security, Image Recognition, Generative Adversarial Networks, AI Deepfake, Fraudulent Activities

PETALface: Parameter Efficient Transfer Learning for Low-resolution Face Recognition

Pre-training on large-scale datasets and utilizing margin-based loss functions have been highly successful in training models for high-resolution face recognition. However, these models struggle with low-resolution face datasets, in which the faces lack the facial attributes necessary for distinguishing different faces. Full fine-tuning on low-resolution datasets, a naive method for adapting the model, yields inferior performance due to catastrophic forgetting of pre-trained knowledge. Additionally the domain difference between high-resolution (HR) gallery images and low-resolution (LR) probe images in low resolution datasets leads to poor convergence for a single model to adapt to both gallery and probe after fine-tuning. To this end, we propose PETALface, a Parameter-Efficient Transfer Learning approach for low-resolution face recognition. Through PETALface, we attempt to solve both the aforementioned problems. (1) We solve catastrophic forgetting by leveraging the power of parameter efficient fine-tuning(PEFT). (2) We introduce two low-rank adaptation modules to the backbone, with weights adjusted based on the input image quality to account for the difference in quality for the gallery and probe images. To the best of our knowledge, PETALface is the first work leveraging the powers of PEFT for low resolution face recognition. Extensive experiments demonstrate that the proposed method outperforms full fine-tuning on low-resolution datasets while preserving performance on high-resolution and mixed-quality datasets, all while using only 0.48% of the parameters. Code: https://kartik-3004.github.io/PETALface/

Gradient-based facial encoding for key generation to encrypt and decrypt multimedia data

Password-based security is prone to forgetting, guessing, and hacking. Similarly, standalone biometric-based security is susceptible to template spoofing and replay attacks. This paper proposes a biocryptosystem based on face recognition technique to bridge this gap such that it can encrypt and decrypt any kind of file using the Advanced Encryption Standard (AES). The biocryptosystem uses a combination of biometric identification and cryptographic methods to protect sensitive information in a secure and effective manner. To verify a user's identity, our proposed system first captures an image of their face and extracts facial traits. The Histogram of Oriented Gradients (HOG) detects all the unique facial traits because HOG effectively captures edge-based features even in dim lighting. Every data type, including text, audio, and video files, can be encrypted and decrypted using this system. Biometric evidence is inherently tied to an individual, so it is almost impossible for attackers to access the user's data. This method also offers a high level of security by employing biometric data as an element in the 2-factor authentication process. The precision, efficiency, and security of this biocryptosystem are experimentally proven by different metrics like entropy and avalanche effect. Applications for the proposed system include safe file sharing, online transactions, and data archiving. Hence, it offers a strong and dependable option for safeguarding sensitive data.

Emotion Classification of Children Expressions

This paper proposes a process for a classification model for the facial expressions. The proposed process would aid in specific categorisation of children's emotions from 2 emotions namely 'Happy' and 'Sad'. Since the existing emotion recognition systems algorithms primarily train on adult faces, the model developed is achieved by using advanced concepts of models with Squeeze-andExcitation blocks, Convolutional Block Attention modules, and robust data augmentation. Stable Diffusion image synthesis was used for expanding and diversifying the data set generating realistic and various training samples. The model designed using Batch Normalisation, Dropout, and SE Attention mechanisms for the classification of children's emotions achieved an accuracy rate of 89\% due to these methods improving the precision of emotion recognition in children. The relative importance of this issue is raised in this study with an emphasis on the call for a more specific model in emotion detection systems for the young generation with specific direction on how the young people can be assisted to manage emotions while online.