Ensuring auditability and verifiability in Federated Learning (FL) is both challenging and essential to guarantee that local data remains untampered and client updates are trustworthy. Recent FL frameworks assess client contributions through a trusted central server using various client selection and aggregation techniques. However, reliance on a central server can create a single point of failure, making it vulnerable to privacy-centric attacks and limiting its ability to audit and verify client-side data contributions due to restricted access. In addition, data quality and fairness evaluations are often inadequate, failing to distinguish between high-impact contributions and those from low-quality or poisoned data. To address these challenges, we propose Federated Auditable and Verifiable Data valuation (FAVD), a privacy-preserving method that ensures auditability and verifiability of client contributions through data valuation, independent of any central authority or predefined training algorithm. FAVD utilizes shared local data density functions to construct a global density function, aligning data contributions and facilitating effective valuation prior to local model training. This proactive approach improves transparency in data valuation and ensures that only benign updates are generated, even in the presence of malicious data. Further, to mitigate privacy risks associated with sharing data density functions, we add Gaussian noise to each client’s local density function before sharing it with the server. We theoretically demonstrate the convergence, auditability, and verifiability of FAVD, along with its resilience against data poisoning threats. Our experiments on five diverse benchmarks, including three medical datasets, show that FAVD achieves significant performance gains, accurate data valuation, and fair client contributions under threat, highlighting its reliability as a trustworthy FL approach.

Low-dose computed tomography (LDCT) scans have become a widely adopted technique to minimize radiation exposure in medical imaging. However, a lower radiation dose introduces noise, which degrades image quality and compromises diagnostic precision. The denoising of LDCT scans remains a critical challenge in the field of computed tomography (CT) imaging research. To address this, efficient denoising techniques such as deep learning methods have been proposed for LDCT denoising, primarily using high-dose CT (HDCT) images as ground truth. However, these methods often lead to over-smoothing and fail to capture the inherent spatial correlations within a single CT slice, particularly the anatomical semantics. To address these challenges, we propose NoisConFuse-Net, a robust deep learning framework for LDCT denoising. The core architecture features a Perturbed Encoder with Complementary Dual Decoders (PE-CDD), designed around efficient self-attention (ESA) mechanism to capture the local as well as global dependencies in the slice. Additionally, a Noise-Augmented Feature Maps (NAFM) module is introduced to enhance network generalization for image denoising by perturbing intermediate feature representations within the encoder. Our framework employs a dual decoder network for complementary learning strategy that effectively disentangles and balances noise suppression and content preservation. Further, we integrate a pretrained CLIP ResNet image encoder as a contrastive regularizer (CR) to refine the fused output from the PE-CDD, ensuring alignment between the denoised images and HDCT ground truth. This contrastive regularization preserves key anatomical structures while effectively differentiating noise in LDCT images. This novel approach outperforms the state-of-the-art methods, demonstrating its effectiveness for high-quality LDCT denoising.

Anomaly detection is needed in many applications, such as video surveillance, manufacturing defect detection, and medical image analysis. However, this poses a significant challenge as anomalies can exhibit diverse patterns. Furthermore, the scarcity of labelled anomaly (defective) images complicates the use of supervised classification approaches. To address this, alternative approaches such as unsupervised or self-supervised learning are explored, effectively overcoming challenges posed by limited labelled data. While several techniques exist, there is still significant room for improvement in overall detection results, especially in handling noisy images, which is a realistic dimension often overlooked in existing methods. Therefore, we have proposed a novel framework consisting of various modules, including the Attention-Aware PDE constraint Feature Denoiser Module, Teacher, Student, and Anomaly-Attenuator. Here, we have utilized the loss function in a novel manner, which improves overall performance and ensures consistency. Additionally, we train the network in such a way that a single model would work for multiple types of objects. Finally, we considered different datasets for testing and observed that our model provides superior results, outperforming existing state-of-the-art methods for noisy and clean images.

Biometric-based personal authentication systems have seen a strong demand mainly due to the increasing concern in various privacy and security applications. Although the use of each biometric trait is problem dependent, the human ear has been found to have enough discriminating characteristics to allow its use as a strong biometric measure. To locate an ear in a 2D side face image is a challenging task, numerous existing approaches have achieved significant performance, but the majority of studies are based on the constrained environment. However, ear biometrics possess a great level of difficulties in the unconstrained environment, where pose, scale, occlusion, illuminations, background clutter etc. varies to a great extent. To address the problem of ear localization in the wild, we have proposed two high-performance region of interest (ROI) segmentation models UESegNet-1 and UESegNet-2, which are fundamentally based on deep convolutional neural networks and primarily uses contextual information to localize ear in the unconstrained environment. Additionally, we have applied state-of-the-art deep learning models viz; FRCNN (Faster Region Proposal Network) and SSD (Single Shot MultiBox Detecor) for ear localization task. To test the model's generalization, they are evaluated on six different benchmark datasets viz; IITD, IITK, USTB-DB3, UND-E, UND-J2 and UBEAR, all of which contain challenging images. The performance of the models is compared on the basis of object detection performance measure parameters such as IOU (Intersection Over Union), Accuracy, Precision, Recall, and F1-Score. It has been observed that the proposed models UESegNet-1 and UESegNet-2 outperformed the FRCNN and SSD at higher values of IOUs i.e. an accuracy of 100\% is achieved at IOU 0.5 on majority of the databases.

Attention Deficit Hyperactivity Disorder (ADHD) is a behavior-based disorder that mainly occurs in young children. Resting-state fMRI data have been very popular for diagnosing brain disorders like Autism, ADHD, and schizophrenia, by network-based functional connectivity, since these disorders are associated with both individual brain regions and their connections. Finding patterns among regions of controls' brain and ADHD patients' discriminating brains, is a non-trivial task. For classification of ADHD, we propose an end-to-end lightweight CNN architecture with hierarchical representation learning i.e., HLGSNet. We extract 116 anatomical regions from each subject in both normal and patient conditions, and graphs are built with the help of temporal correlation between different regions, where each region is considered as a node. Following this, a Siamese graph convolution neural network with triplet loss has been trained for finding embeddings so that samples for the same class should have similar embeddings. Finally, along with a fully connected layer, the trained model has been fine-tuned for the classification task. Experiments have been carried out on publicly available ADHD-200 dataset with promising performance.

We address the task of estimating depth from a single intensity image via a novel convolutional neural network (CNN) encoder-decoder architecture, which learns the depth information using example pairs of color images and their corresponding depth maps. The proposed model integrates residual connections within pooling and up-sampling layers, and hourglass networks which operate on the encoded features, thus processing these at various scales. Furthermore, the model is optimized under the constraints of perceptual as well as the mean squared error loss. The perceptual loss considers the high-level features, thus operating at a different scale of abstraction, which is complementary to the mean squared error loss. The improvements in qualitative and quantitative comparisons with state-of-the-art approaches demonstrate the effectiveness of our approach, even in presence of noise.