In recent years, significant advancements in the field of machine learning have influenced the domain of image restoration. While these technological advancements present prospects for improving the quality of images, they also present difficulties, particularly the proliferation of manipulated or counterfeit multimedia information on the internet. The objective of this paper is to provide a comprehensive review of existing inpainting algorithms and forgery detections, with a specific emphasis on techniques that are designed for the purpose of removing objects from digital images. In this study, we will examine various techniques encompassing conventional texture synthesis methods as well as those based on neural networks. Furthermore, we will present the artifacts frequently introduced by the inpainting procedure and assess the state-of-the-art technology for detecting such modifications. Lastly, we shall look at the available datasets and how the methods compare with each other. Having covered all the above, the outcome of this study is to provide a comprehensive perspective on the abilities and constraints of detecting object removal via the inpainting procedure in images.

Sound is the result of mechanical vibrations that set air molecules in motion, causing variations in air pressure that propagate as pressure waves. Represented as waveforms, these visual snapshots of sound reveal some of its characteristics. While waveform analysis offers limited insights, audio features provide a quantitative and structured way to describe sound, enabling data-driven analysis and interpretation. Different audio features capture various aspects of sound, facilitating a comprehensive understanding of the audio data. By leveraging audio features, machine learning models can be trained to recognize patterns, classify sounds, or make predictions, enabling the development of intelligent audio systems. Time-domain features, e.g., amplitude envelope, capture events from raw audio waveforms. Frequency domain features, like band energy ratio and spectral centroid, focus on frequency components, providing distinct information. In this paper, we will describe three time-domain and three frequency-domain features that we consider crucial and widely used. We will illustrate the suitability of each feature for specific tasks and draw general conclusions regarding the significance of sound features in the context of machine learning.

Employing machine learning algorithms in the medical field has proven successful for some time now. Mostly computer vision techniques have been applied to medical images, while medical sound data has been somewhat overlooked. By using electronic stethoscopes, it is now possible to process both heartbeats and lung sounds. While some products are available for detecting anomalies in heartbeats, addressing lung-related anomalies presents a more intricate challenge. Applying a deep learning approach is hindered by insufficient data. Although some datasets do exist, the size and diversity of the data are too small for comprehensive analysis. This paper introduces a novel technique for detecting anomalies in lung sounds: first by combining two datasets, second by automatically segmenting each sound into respiratory cycles, and third by employing GFCCs as sound features.

In recent years, significant advancements in the field of machine learning have influenced the domain of image restoration. While these technological advancements present prospects for improving the quality of images, they also present difficulties, particularly the proliferation of manipulated or counterfeit multimedia information on the internet. The objective of this paper is to provide a comprehensive review of existing inpainting algorithms and forgery detections, with a specific emphasis on techniques that are designed for the purpose of removing objects from digital images. In this study, we will examine various techniques encompassing conventional texture synthesis methods, as well as those based on neural networks. Furthermore, we will explore the artifacts associated with the identification of modified photos and present the artifacts frequently introduced by the inpainting procedure and assess the state-of-the-art technology for detecting such modifications. Lastly, we shall look at the available datasets and how the methods compare with each other. Having covered all of the above, the final outcome of this study is to provide a comprehensive perspective on the abilities and constraints to detect images for which an inpainting object removal method was applied.