Image-based AI models are increasingly deployed across a wide range of domains, including healthcare, security, and consumer applications. However, many image datasets carry sensitive or proprietary content, raising critical concerns about unauthorized data usage. Data owners therefore need reliable mechanisms to verify whether their proprietary data has been misused to train third-party models. Existing solutions, such as backdoor watermarking and membership inference, face inherent trade-offs between verification effectiveness and preservation of data integrity. In this work, we propose HoneyImage, a novel method for dataset ownership verification in image recognition models. HoneyImage selectively modifies a small number of hard samples to embed imperceptible yet verifiable traces, enabling reliable ownership verification while maintaining dataset integrity. Extensive experiments across four benchmark datasets and multiple model architectures show that HoneyImage consistently achieves strong verification accuracy with minimal impact on downstream performance while maintaining imperceptible. The proposed HoneyImage method could provide data owners with a practical mechanism to protect ownership over valuable image datasets, encouraging safe sharing and unlocking the full transformative potential of data-driven AI.

Image recognition models have struggled to treat recognition robustness to real-world degradations. In this context, data augmentation methods like PixMix improve robustness but rely on generative arts and feature visualizations (FVis), which have copyright, drawing cost, and scalability issues. We propose MoireDB, a formula-generated interference-fringe image dataset for image augmentation enhancing robustness. MoireDB eliminates copyright concerns, reduces dataset assembly costs, and enhances robustness by leveraging illusory patterns. Experiments show that MoireDB augmented images outperforms traditional Fractal arts and FVis-based augmentations, making it a scalable and effective solution for improving model robustness against real-world degradations.

Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to sub-optimal mapping on hardware accelerators during model deployment, which may lead to timing uncertainty and erroneous behavior. Mapping on hardware accelerators is done using multiple software components like deep learning frameworks, compilers, and device libraries, that we refer to as the computational environment. Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment, as the impact of parameters like deep learning frameworks, compiler optimizations, and hardware devices on model performance and correctness is not yet well understood. In this paper we present a differential testing framework, DeltaNN, that allows us to assess the impact of different computational environment parameters on the performance of image recognition models during deployment, post training. DeltaNN generates different implementations of a given image recognition model for variations in environment parameters, namely, deep learning frameworks, compiler optimizations and hardware devices and analyzes differences in model performance as a result. Using DeltaNN, we conduct an empirical study of robustness analysis of three popular image recognition models using the ImageNet dataset. We report the impact in terms of misclassifications and inference time differences across different settings. In total, we observed up to 72% output label differences across deep learning frameworks, and up to 81% unexpected performance degradation in terms of inference time, when applying compiler optimizations.

Building an Image Recognition Model using TensorFlow and Keras Libraries in Python Image recognition models are extremely useful in a wide range of applications, from autonomous vehicles and medical diagnosis to social media analysis and e-commerce. By teaching a computer to identify and classify images based on certain features, such as color, shape, and texture, we can automate tasks that would be difficult or impossible for humans to do at scale. For example, an image recognition model can be used to detect objects in images, recognize faces and emotions, identify text in images, and even diagnose medical conditions based on medical images. In e-commerce, image recognition models can be used to recommend products based on visual similarity, allowing for more personalized and relevant product recommendations. Pretty cool, right? Let’s give it a try… Step 1. Install the required libraries: First, you need to install TensorFlow and Keras libraries in Python. You can install them using pip command in the terminal. pip install tensorflow pip install keras Step 2. Import the required libraries: Once the libraries are installed, you need to import them in your Python script. import tensorflow as tf from tensorflow import keras Step 3. Load the dataset: Next, […]

Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and FPGAs for fast, timely processing. Failure in real-time image recognition tasks can occur due to incorrect mapping on hardware accelerators, which may lead to timing uncertainty and incorrect behavior. Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment as parameters like deep learning frameworks, compiler optimizations for code generation, and hardware devices are not regulated with varying impact on model performance and correctness. In this paper we conduct robustness analysis of four popular image recognition models (MobileNetV2, ResNet101V2, DenseNet121 and InceptionV3) with the ImageNet dataset, assessing the impact of the following parameters in the model's computational environment: (1) deep learning frameworks; (2) compiler optimizations; and (3) hardware devices. We report sensitivity of model performance in terms of output label and inference time for changes in each of these environment parameters. We find that output label predictions for all four models are sensitive to choice of deep learning framework (by up to 57%) and insensitive to other parameters. On the other hand, model inference time was affected by all environment parameters with changes in hardware device having the most effect. The extent of effect was not uniform across models.

In this article, we will look at the development of AI and the field of deep learning. Deep learning originated in the era of vacuum tube computers. In 1958, Frank Rosenblatt of Cornell University designed the first artificial neural network. This was later named "deep learning". Rosenblatt knew that this technology surpassed the computing power at that time.

Congratulations, it's going be a rough one. Broadly speaking, patents can be afforded to systems, apparatuses and processes. So why is patenting AI rough? Because patent language describes inventions in terms of what does what, why, when and how. In other words, you need to describe your AI invention in terms of structure.

PyTorch Lightning lets researchers build their own Deep Learning models quickly & easily without having to worry about the complexities. This book will help you maximize productivity for Deep Learning projects while ensuring full flexibility from model formulation to implementation. PyTorch has been trending upwards since 2019 and has been slowly replacing TensorFlow. In trend in the research community has been as clear as daylight about which is the most preferred platform with majority of papers published in PyTorch. The book provides a hands-on approach for implementing PyTorch Lightning DL models and associated methodologies that will have you up and running and productive in no time. You'll learn how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions.

Object recognition is a subfield of computer vision, artificial intelligence, and machine learning that seeks to recognize and identify the most prominent objects (i.e., people or things) in a digital image or video with AI models. Image recognition is also a subfield of AI and computer vision that seeks to recognize the high level contents of an image. If you're familiar with the domain of computer vision, you might think that object recognition sounds very similar to a related task: image recognition. However, there's a subtle yet important difference between image recognition and object recognition: The best way to illustrate the difference between object recognition and image recognition is through an example. Given a photograph of a soccer game, an image recognition model would return a single label such as "soccer game."

There are many ways in which data can reflect biases. Data collection suffers from different biases that can result in the underrepresentation or overrepresentation of certain groups, populations or categories. This is especially the case when multiple data sets are combined and used in aggregate. Data might become tainted through the under-selection or over-selection of certain communities, groups or races. Give extra attention to historical data, especially in areas that have been riddled with prejudicial bias, to make sure new models created with this data don't incorporate those historical biases or injustices.