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Attackers may manipulate audio with the intent of presenting falsified reports, changing an opinion of a public figure, and winning influence and power. The prevalence of inauthentic multimedia continues to rise, so it is imperative to develop a set of tools that determines the legitimacy of media. We present a method that analyzes audio signals to determine whether they contain real human voices or fake human voices (i.e., voices generated by neural acoustic and waveform models). Instead of analyzing the audio signals directly, the proposed approach converts the audio signals into spectrogram images displaying frequency, intensity, and temporal content and evaluates them with a Convolutional Neural Network (CNN). Trained on both genuine human voice signals and synthesized voice signals, we show our approach achieves high accuracy on this classification task., Comment: Accepted to the 2021 Media Watermarking, Security, and Forensics Conference, IS&T Electronic Imaging Symposium (EI)

Estimating building height from satellite imagery is important for digital surface modeling while also providing rich information for change detection and building footprint detection. The acquisition of building height usually requires a LiDAR system, which is not often available in many satellite systems. In this paper, we describe a building height estimation method that does not require building height annotation. Our method estimates building height using building shadows and satellite image metadata given a single RGB satellite image. To reduce the data annotation needed, we design a multi-stage instance detection method for building and shadow detection with both supervised and semi-supervised training. Given the detected building and shadow instances, we can then estimate the building height with satellite image metadata. Building height estimation is done by maximizing the overlap between the projected shadow region given a query height and the detected shadow region. We evaluate our method on the xView2 and Urban Semantic 3D datasets and show that the proposed method achieves accurate building detection, shadow detection, and height estimation.

Compared to conventional ballistic vehicles and cruise vehicles, hypersonic vehicles exhibit unprecedented and clearly superior abilities. Hypersonic glide vehicles (HGVs) travel at speeds faster than Mach 5, enabling them to fly at least one mile per second. Furthermore, they possess maneuvering capabilities that assist them in evading defense systems, increasing precision of their impact points, and hindering prediction of their final destinations. In this paper, we examine machine learning methods to automatically identify different hypersonic glide vehicles and a ballistic reentry vehicle (RV) based on trajectory segments. Trained on aerodynamic state estimates, our methods analyze key vehicle maneuvers to classify vehicles with high accuracy. We also identify vehicles with higher accuracy as time after liftoff (TALO) increases and more data becomes available for analysis.

Altered and manipulated multimedia is increasingly present and widely distributed via social media platforms. Advanced video manipulation tools enable the generation of highly realistic-looking altered multimedia. While many methods have been presented to detect manipulations, most of them fail when evaluated with data outside of the datasets used in research environments. In order to address this problem, the Deepfake Detection Challenge (DFDC) provides a large dataset of videos containing realistic manipulations and an evaluation system that ensures that methods work quickly and accurately, even when faced with challenging data. In this paper, we introduce a method based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) that extracts visual and temporal features from faces present in videos to accurately detect manipulations. The method is evaluated with the DFDC dataset, providing competitive results compared to other techniques.

When disaster strikes, accurate situational information and a fast, effective response are critical to save lives. Widely available, high resolution satellite images enable emergency responders to estimate locations, causes, and severity of damage. Quickly and accurately analyzing the extensive amount of satellite imagery available, though, requires an automatic approach. In this paper, we present Siam-U-Net-Attn model - a multi-class deep learning model with an attention mechanism - to assess damage levels of buildings given a pair of satellite images depicting a scene before and after a disaster. We evaluate the proposed method on xView2, a large-scale building damage assessment dataset, and demonstrate that the proposed approach achieves accurate damage scale classification and building segmentation results simultaneously., 10 pages, 9 figures

The widespread availability of image editing tools and improvements in image processing techniques allow image manipulation to be very easy. Oftentimes, easy-to-use yet sophisticated image manipulation tools yields distortions/changes imperceptible to the human observer. Distribution of forged images can have drastic ramifications, especially when coupled with the speed and vastness of the Internet. Therefore, verifying image integrity poses an immense and important challenge to the digital forensic community. Satellite images specifically can be modified in a number of ways, including the insertion of objects to hide existing scenes and structures. In this paper, we describe the use of a Conditional Generative Adversarial Network (cGAN) to identify the presence of such spliced forgeries within satellite images. Additionally, we identify their locations and shapes. Trained on pristine and falsified images, our method achieves high success on these detection and localization objectives., Accepted to the 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)