The higher the chemical diversity and structural complexity of two-dimensional (2D) materials, the higher the likelihood they possess unique and useful properties. Herein, density functional theory (DFT) is used to predict the existence of two new families of 2D ordered, carbides (MXenes), M'2M″C2 and M'2M″2C3, where M' and M″ are two different early transition metals. In these solids, M' layers sandwich M″ carbide layers. By synthesizing Mo2TiC2Tx, Mo2Ti2C3Tx, and Cr2TiC2Tx (where T is a surface termination), we validated the DFT predictions. Since the Mo and Cr atoms are on the outside, they control the 2D flakes' chemical and electrochemical properties. The latter was proven by showing quite different electrochemical behavior of Mo2TiC2Tx and Ti3C2Tx. This work further expands the family of 2D materials, offering additional choices of structures, chemistries, and ultimately useful properties.

Herein, we report on the phase stabilities and crystal structures of two newly discovered ordered, quaternary MAX phases-Mo 2 TiAlC 2 and Mo 2 Ti 2 AlC 3 -synthesized by mixing and heating different elemental powder mixtures of mMo:(3-m)Ti:1.1Al:2C with 1.5 m 2.2 and 2Mo: 2Ti:1.1Al:2.7C to 1600 C for 4 h under Ar flow. In general, for m ! 2 an ordered 312 phase, (Mo 2 Ti)AlC 2 , was the majority phase; for m < 2, an ordered 413 phase (Mo 2 Ti 2 )AlC 3 , was the major product. The actual chemistries determined from X-ray photoelectron spectroscopy (XPS) are Mo 2 TiAlC 1.7 and Mo 2 Ti 1.9 Al 0.9 C 2.5 , respectively. High resolution scanning transmission microscopy, XPS and Rietveld analysis of powder X-ray diffraction confirmed the general ordered stacking sequence to be Mo-Ti-Mo-Al-Mo-Ti-Mo for Mo 2 TiAlC 2 and Mo-Ti-Ti-Mo-Al-Mo-Ti-TiMo for Mo 2 Ti 2 AlC 3 , with the carbon atoms occupying the octahedral sites between the transition metal layers. Consistent with the experimental results, the theoretical calculations clearly show that M layer ordering is mostly driven by the high penalty paid in energy by having the Mo atoms surrounded by C in a face-centered configuration, i.e., in the center of the M nþ1 X n blocks. At 331 GPa and 367 GPa, respectively, the Young's moduli of the ordered Mo 2 TiAlC 2 and Mo 2 Ti 2 AlC 3 are predicted to be higher than those calculated for their ternary end members. Like most other MAX phases, because of the high density of states at the Fermi level, the resistivity measurement over 300 to 10 K for both phases showed metallic behavior. V C 2015 AIP Publishing LLC.

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Recent advances in deep learning and computer vision have spawned a new class of media forgeries known as deepfakes, which typically consist of artificially generated human faces or voices. The creation and distribution of deepfakes raise many legal and ethical concerns. As a result, the ability to distinguish between deepfakes and authentic media is vital. While deepfakes can create plausible video and audio, it may be challenging for them to to generate content that is consistent in terms of high-level semantic features, such as emotions. Unnatural displays of emotion, measured by features such as valence and arousal, can provide significant evidence that a video has been synthesized.In this paper, we propose a novel method for detecting deepfakes of a human speaker using the emotion predicted from the speaker's face and voice. The proposed technique leverages Long Short-Term Memory (LSTM) networks that predict emotion from audio and video Low-Level Descriptors (LLDs). Predicted emotion in time is used to classify videos as authentic or deepfakes through an additional supervised classifier.

Modern technologies have made the capture and sharing of digital video commonplace; the combination of modern smartphones, cloud storage, and social media platforms have enabled video to become a primary source of information for many people and institutions. As a result, it is important to be able to verify the authenticity and source of this information, including identifying the source camera model that captured it. While a variety of forensic techniques have been developed for digital images, less research has been conducted toward the forensic analysis of videos. In part, this is due to a lack of standard digital video databases, which are necessary to develop and evaluate state-of-the-art video forensic algorithms. In this paper, to address this need, we present the video authentication and camera identification (video-ACID) database, a large collection of videos specifically collected for the development of camera model identification algorithms. The video-ACID database contains over 12 000 videos from 46 physical devices representing 36 unique camera models. Videos in this database are hand collected in a diversity of real-world scenarios are unedited and have known and trusted provenance. In this paper, we describe the qualities, structure, and collection procedure of video-ACID, which includes clearly marked videos for evaluating camera model identification algorithms. Finally, we provide baseline camera model identification results on these evaluation videos using the state-of-the-art deep-learning techniques. The Video-ACID database is publicly available at misl.ece.drexel.edu/video-acid. INDEX TERMS Forensics, multimedia databases, benchmark testing, video signal processing. I. INTRODUCTION The capture and spread of digital multimedia has exploded over the last several decades. Higher quality and wildly available cameras, like those in modern smartphones, as well as internet applications, such as social media and cloud storage, have allowed the average person to easily document anything from a family vacation to an academic lecture. However, in some scenarios, such as news reporting, legal proceedings, and national security operations, it is critical to know the source and integrity of a given image or video. The associate editor coordinating the review of this manuscript and approving it for publication was Irene Amerini. To address these issues, researchers have developed forensic algorithms that verify the authenticity and source of digital content [1]-[4]. For example, techniques have been developed to identify the processing history of digital images [5]-[12], perform image forgery detection [13]-[24], as well to identify an image's source device [25]-[27] and source camera model [28]-[34]. The development of these algorithms has been significantly aided by the availability of several, high quality forensic databases, such as the Dresden Image Database [35] and the Vision Database [36]. While much of forensics research has focused on images, the increasing importance of video has created a growing