Detecting camouflaged objects is crucial in various applications such as military surveillance, wildlife conservation, and in search and rescue operations. However, the limited availability of camouflaged object data poses a significant challenge in developing accurate detection models. This paper proposes a quasi-synthetic data generation by image compositing combined with attention-based deep learning-based harmonization methodology to generate feature-enriched realistic images for camouflaged objects under varying scenarios. In our work, we developed a diverse set of images to simulate different environmental conditions, including lighting, shadows, fog, dust, and snow, to test our proposed methodology. The intention of generating such photo-realistic images is to increase the robustness of the model with the additional benefit of data augmentation for training our camouflaged object detection model(COD). Furthermore, we evaluate our approach using state-of-the-art object detection models and demonstrate that training with our quasi-synthetic images can significantly improve the detection accuracy of camouflaged objects under varying conditions. Additionally, to test the real operational performance of the developed models, we deployed the models on resource-constrained edge devices for real-time object detection to validate the performance of the trained model on quasi-synthetic data compared to the synthetic data generated by conventional neural style transfer architecture.
Federated Learning (FL) enables collaborative model building among a large number of participants without revealing the sensitive data to the central server. However, because of its distributed nature, FL has limited control over the local data and corresponding training process. Therefore, it is susceptible to data poisoning attacks where malicious workers use malicious training data to train the model. Furthermore, attackers on the worker side can easily manipulate local data by swapping the labels of training instances to initiate data poisoning attacks. And local workers under such attacks carry incorrect information to the server, poison the global model, and cause misclassifications. So, detecting and preventing poisonous training samples from local training is crucial in federated training. To address it, we propose a federated learning framework, namely Confident Federated Learning to prevent data poisoning attacks on local workers. Here, we first validate the label quality of training samples by characterizing and identifying label errors in the training data and then exclude the detected mislabeled samples from the local training. To this aim, we experiment with our proposed approach on MNIST, Fashion-MNIST, and CIFAR-10 dataset and experimental results validated the robustness of the proposed framework against the data poisoning attacks by successfully detecting the mislabeled samples with above 85% accuracy.
KEYWORDS: Data modeling, Computer security, Visualization, Neurons, Instrument modeling, Data communications, Acoustics, Profiling, Data acquisition, Visual process modeling
Secure data communication is crucial in contested environments such as battlefields. In such environments, there is always risk of data breach through unauthorized interceptions. This may lead to unauthorized access to tactical information and infiltration into the systems. In this work, we propose a detailed training setup in the federated learning framework for object classification where the raw data will be maintained locally at the edge devices and will not be shared with a central server or with each other. The server sends a global model to edge devices, which is then trained locally at the edge, and the updated parameters are sent back to the central server, where they are aggregated, which takes place iteratively. This setup ensures robustness against malicious cyberattacks as well as reduce communication overhead. Furthermore, to tackle the irregularity in object classification task with a single data modality in such contested environment, a deep learning model incorporating multiple modalities is used as the global model in our proposed federated learning setup. This model can serve as a possible solution in object identification with multi-modal data. We conduct a comprehensive analysis on the importance of multi-modal approach compared to individual modalities within our proposed federate learning setup. We also provide a resource profiling based on memory requirements, training time, and energy usage on two resource constrained devices to demonstrate the feasibility of the proposed approach.
Deep Learning (DL) requires a massive, labeled dataset for supervised semantic segmentation. Getting massive labeled data under a new setting (target domain) to perform semantic segmentation requires huge efforts in time and resources. One possible solution is domain adaptation (DA) where researchers transform the data distribution of existent annotated public data (source domain) to resemble the target domain. We develop a model on this transformed data. Nevertheless, this poses the questions of what source domain/s to utilize, and what types of transformation to perform on that domain/s. In this research work, we study those answers by benchmarking different data transformation approaches on source-only and single-source domain adaptation setups. We provide a new well-suited dataset using unmanned ground vehicle Husarion ROSbot 2.0 to analyze and demonstrate the relative performance of different DA approaches.
Camouflage is the art of deception which is often used in the animal world. It is also used on the battlefield to hide military assets. Camouflaged objects hide within their environments by taking on colors and textures that are similar to their surroundings. In this work, we explore the classification and localization of camouflaged enemy assets including soldiers. In this paper we address two major challenges: a) how to overcome the paucity of domain-specific labeled data and b) how to perform camouflage object detection using edge devices. To address the first challenge, we develop a deep neural style transfer model that blends content images of objects such as soldiers, tanks, and mines/improvised explosive devices with style images depicting deserts, jungles, and snow-covered regions. To address the second challenge, we develop combined depth-guided deep neural network models that combine image features with depth features. Previous research suggests that depth features not only contain local information about object geometry but also provide information on the position, and shape for camouflaged object identification and localization. In this work, we use precomputed monocular method for the generation of the depth maps. The novel fusion-based architecture provides an efficient representation learning space for object detection. In addition, we perform ablation studies to measure the performance of depth versus RGB in detecting camouflaged objects. We also demonstrate how such as model can be deployed in edge devices for real-time object identification and localization.
The purpose of this study is to devise a Computer Aided Diagnosis (CAD) system that is able to detect COVID-19 abnormalities from chest radio-graphs with increased efficiency and accuracy. We investigate a novel deep learning based ensemble model to classify the category of pneumonia from chest X-ray images. We use a labeled image dataset provided by Society for Imaging Informatics in Medicine for a kaggle competition that contains chest radio-graphs. And the task of our proposed CAD is to categorize between negative for pneumonia or typical, indeterminate, atypical for COVID-19. The training set (with labels publicly available) of this dataset contains 6334 images belonging to 4 classes. Furthermore, we experiment on the efficacy of our proposed ensemble method. Accordingly, we perform a ablation study to confirm that our proposed pipeline drives the classification accuracy higher and also compare our ensemble technique with the existing ones quantitatively and qualitatively.
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