Npix2Cpix: A GAN-based Image-to-Image Translation Network with Retrieval-Classification Integration for Watermark Retrieval from Historical Document Images

The identification and restoration of ancient watermarks have long been a major topic in codicology and history. Classifying historical documents based on watermarks can be difficult due to the diversity of watermarks, crowded and noisy samples, multiple modes of representation, and minor distinctions between classes and intra-class changes. This paper proposes a U-net-based conditional generative adversarial network (GAN) to translate noisy raw historical watermarked images into clean, handwriting-free images with just watermarks. Considering its ability to perform image translation from degraded (noisy) pixels to clean pixels, the proposed network is termed as Npix2Cpix. Instead of employing directly degraded watermarked images, the proposed network uses image-to-image translation using adversarial learning to create clutter and handwriting-free images for restoring and categorizing the watermarks for the first time. In order to learn the mapping from input noisy image to output clean image, the generator and discriminator of the proposed U-net-based GAN are trained using two separate loss functions, each of which is based on the distance between images. After using the proposed GAN to pre-process noisy watermarked images, Siamese-based one-shot learning is used to classify watermarks. According to experimental results on a large-scale historical watermark dataset, extracting watermarks from tainted images can result in high one-shot classification accuracy. The qualitative and quantitative evaluation of the retrieved watermarks illustrates the effectiveness of the proposed approach.

Decoding Human Activities: Analyzing Wearable Accelerometer and Gyroscope Data for Activity Recognition

A person's movement or relative positioning effectively generates raw electrical signals that can be read by computing machines to apply various manipulative techniques for the classification of different human activities. In this paper, a stratified multi-structural approach based on a Residual network ensembled with Residual MobileNet is proposed, termed as FusionActNet. The proposed method involves using carefully designed Residual blocks for classifying the static and dynamic activities separately because they have clear and distinct characteristics that set them apart. These networks are trained independently, resulting in two specialized and highly accurate models. These models excel at recognizing activities within a specific superclass by taking advantage of the unique algorithmic benefits of architectural adjustments. Afterward, these two ResNets are passed through a weighted ensemble-based Residual MobileNet. Subsequently, this ensemble proficiently discriminates between a specific static and a specific dynamic activity, which were previously identified based on their distinct feature characteristics in the earlier stage. The proposed model is evaluated using two publicly accessible datasets; namely, UCI HAR and Motion-Sense. Therein, it successfully handled the highly confusing cases of data overlap. Therefore, the proposed approach achieves a state-of-the-art accuracy of 96.71% and 95.35% in the UCI HAR and Motion-Sense datasets respectively.

Syn-Att: Synthetic Speech Attribution via Semi-Supervised Unknown Multi-Class Ensemble of CNNs

With the huge technological advances introduced by deep learning in audio & speech processing, many novel synthetic speech techniques achieved incredible realistic results. As these methods generate realistic fake human voices, they can be used in malicious acts such as people imitation, fake news, spreading, spoofing, media manipulations, etc. Hence, the ability to detect synthetic or natural speech has become an urgent necessity. Moreover, being able to tell which algorithm has been used to generate a synthetic speech track can be of preeminent importance to track down the culprit. In this paper, a novel strategy is proposed to attribute a synthetic speech track to the generator that is used to synthesize it. The proposed detector transforms the audio into log-mel spectrogram, extracts features using CNN, and classifies it between five known and unknown algorithms, utilizing semi-supervision and ensemble to improve its robustness and generalizability significantly. The proposed detector is validated on two evaluation datasets consisting of a total of 18,000 weakly perturbed (Eval 1) & 10,000 strongly perturbed (Eval 2) synthetic speeches. The proposed method outperforms other top teams in accuracy by 12-13% on Eval 2 and 1-2% on Eval 1, in the IEEE SP Cup challenge at ICASSP 2022.

Semi-Supervised Semantic Depth Estimation using Symbiotic Transformer and NearFarMix Augmentation

In computer vision, depth estimation is crucial for domains like robotics, autonomous vehicles, augmented reality, and virtual reality. Integrating semantics with depth enhances scene understanding through reciprocal information sharing. However, the scarcity of semantic information in datasets poses challenges. Existing convolutional approaches with limited local receptive fields hinder the full utilization of the symbiotic potential between depth and semantics. This paper introduces a dataset-invariant semi-supervised strategy to address the scarcity of semantic information. It proposes the Depth Semantics Symbiosis module, leveraging the Symbiotic Transformer for achieving comprehensive mutual awareness by information exchange within both local and global contexts. Additionally, a novel augmentation, NearFarMix is introduced to combat overfitting and compensate both depth-semantic tasks by strategically merging regions from two images, generating diverse and structurally consistent samples with enhanced control. Extensive experiments on NYU-Depth-V2 and KITTI datasets demonstrate the superiority of our proposed techniques in indoor and outdoor environments.

Quantum Convolutional Neural Networks with Interaction Layers for Classification of Classical Data

Quantum Machine Learning (QML) has come into the limelight due to the exceptional computational abilities of quantum computers. With the promises of near error-free quantum computers in the not-so-distant future, it is important that the effect of multi-qubit interactions on quantum neural networks is studied extensively. This paper introduces a Quantum Convolutional Network with novel Interaction layers exploiting three-qubit interactions increasing the network's expressibility and entangling capability, for classifying both image and one-dimensional data. The proposed approach is tested on three publicly available datasets namely MNIST, Fashion MNIST, and Iris datasets, to perform binary and multiclass classifications and is found to supersede the performance of the existing state-of-the-art methods.

CIFF-Net: Contextual Image Feature Fusion for Melanoma Diagnosis

Melanoma is considered to be the deadliest variant of skin cancer causing around 75\% of total skin cancer deaths. To diagnose Melanoma, clinicians assess and compare multiple skin lesions of the same patient concurrently to gather contextual information regarding the patterns, and abnormality of the skin. So far this concurrent multi-image comparative method has not been explored by existing deep learning-based schemes. In this paper, based on contextual image feature fusion (CIFF), a deep neural network (CIFF-Net) is proposed, which integrates patient-level contextual information into the traditional approaches for improved Melanoma diagnosis by concurrent multi-image comparative method. The proposed multi-kernel self attention (MKSA) module offers better generalization of the extracted features by introducing multi-kernel operations in the self attention mechanisms. To utilize both self attention and contextual feature-wise attention, an attention guided module named contextual feature fusion (CFF) is proposed that integrates extracted features from different contextual images into a single feature vector. Finally, in comparative contextual feature fusion (CCFF) module, primary and contextual features are compared concurrently to generate comparative features. Significant improvement in performance has been achieved on the ISIC-2020 dataset over the traditional approaches that validate the effectiveness of the proposed contextual learning scheme.

DwinFormer: Dual Window Transformers for End-to-End Monocular Depth Estimation

Depth estimation from a single image is of paramount importance in the realm of computer vision, with a multitude of applications. Conventional methods suffer from the trade-off between consistency and fine-grained details due to the local-receptive field limiting their practicality. This lack of long-range dependency inherently comes from the convolutional neural network part of the architecture. In this paper, a dual window transformer-based network, namely DwinFormer, is proposed, which utilizes both local and global features for end-to-end monocular depth estimation. The DwinFormer consists of dual window self-attention and cross-attention transformers, Dwin-SAT and Dwin-CAT, respectively. The Dwin-SAT seamlessly extracts intricate, locally aware features while concurrently capturing global context. It harnesses the power of local and global window attention to adeptly capture both short-range and long-range dependencies, obviating the need for complex and computationally expensive operations, such as attention masking or window shifting. Moreover, Dwin-SAT introduces inductive biases which provide desirable properties, such as translational equvariance and less dependence on large-scale data. Furthermore, conventional decoding methods often rely on skip connections which may result in semantic discrepancies and a lack of global context when fusing encoder and decoder features. In contrast, the Dwin-CAT employs both local and global window cross-attention to seamlessly fuse encoder and decoder features with both fine-grained local and contextually aware global information, effectively amending semantic gap. Empirical evidence obtained through extensive experimentation on the NYU-Depth-V2 and KITTI datasets demonstrates the superiority of the proposed method, consistently outperforming existing approaches across both indoor and outdoor environments.

ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection

Synthetic image generation has opened up new opportunities but has also created threats in regard to privacy, authenticity, and security. Detecting fake images is of paramount importance to prevent illegal activities, and previous research has shown that generative models leave unique patterns in their synthetic images that can be exploited to detect them. However, the fundamental problem of generalization remains, as even state-of-the-art detectors encounter difficulty when facing generators never seen during training. To assess the generalizability and robustness of synthetic image detectors in the face of real-world impairments, this paper presents a large-scale dataset named ArtiFact, comprising diverse generators, object categories, and real-world challenges. Moreover, the proposed multi-class classification scheme, combined with a filter stride reduction strategy addresses social platform impairments and effectively detects synthetic images from both seen and unseen generators. The proposed solution significantly outperforms other top teams by 8.34% on Test 1, 1.26% on Test 2, and 15.08% on Test 3 in the IEEE VIP Cup challenge at ICIP 2022, as measured by the accuracy metric.

A Novel Hierarchical-Classification-Block Based Convolutional Neural Network for Source Camera Model Identification

Digital security has been an active area of research interest due to the rapid adaptation of internet infrastructure, the increasing popularity of social media, and digital cameras. Due to inherent differences in working principles to generate an image, different camera brands left behind different intrinsic processing noises which can be used to identify the camera brand. In the last decade, many signal processing and deep learning-based methods have been proposed to identify and isolate this noise from the scene details in an image to detect the source camera brand. One prominent solution is to utilize a hierarchical classification system rather than the traditional single-classifier approach. Different individual networks are used for brand-level and model-level source camera identification. This approach allows for better scaling and requires minimal modifications for adding a new camera brand/model to the solution. However, using different full-fledged networks for both brand and model-level classification substantially increases memory consumption and training complexity. Moreover, extracted low-level features from the different network's initial layers often coincide, resulting in redundant weights. To mitigate the training and memory complexity, we propose a classifier-block-level hierarchical system instead of a network-level one for source camera model classification. Our proposed approach not only results in significantly fewer parameters but also retains the capability to add a new camera model with minimal modification. Thorough experimentation on the publicly available Dresden dataset shows that our proposed approach can achieve the same level of state-of-the-art performance but requires fewer parameters compared to a state-of-the-art network-level hierarchical-based system.

A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network

Deep neural network is an effective choice to automatically recognize human actions utilizing data from various wearable sensors. These networks automate the process of feature extraction relying completely on data. However, various noises in time series data with complex inter-modal relationships among sensors make this process more complicated. In this paper, we have proposed a novel multi-stage training approach that increases diversity in this feature extraction process to make accurate recognition of actions by combining varieties of features extracted from diverse perspectives. Initially, instead of using single type of transformation, numerous transformations are employed on time series data to obtain variegated representations of the features encoded in raw data. An efficient deep CNN architecture is proposed that can be individually trained to extract features from different transformed spaces. Later, these CNN feature extractors are merged into an optimal architecture finely tuned for optimizing diversified extracted features through a combined training stage or multiple sequential training stages. This approach offers the opportunity to explore the encoded features in raw sensor data utilizing multifarious observation windows with immense scope for efficient selection of features for final convergence. Extensive experimentations have been carried out in three publicly available datasets that provide outstanding performance consistently with average five-fold cross-validation accuracy of 99.29% on UCI HAR database, 99.02% on USC HAR database, and 97.21% on SKODA database outperforming other state-of-the-art approaches.