Femtosecond x-ray absorption spectroscopy of spin and orbital angular momentum in photoexcited Ni films during ultrafast demagnetization

We follow for the first time the evolution of the spin and orbital angular momentum of a thin Ni film during ultrafast demagnetization, by means of x-ray magnetic circular dichroism. Both components decrease with a 130 +/- 40 fs time constant upon excitation with a femtosecond laser pulse. Additional x-ray absorption measurements reveal an increase in the spin-orbit interaction by 6 +/- 2 % during this process. This is the experimental demonstration quantifying the importance of spin-orbit mediated processes during the demagnetization

Exposing Fake Images with Forensic Similarity Graphs

We propose new image forgery detection and localization algorithms by recasting these problems as graph-based community detection problems. To do this, we introduce a novel abstract, graph-based representation of an image, which we call the Forensic Similarity Graph, that captures key forensic relationships among regions in the image. In this representation, small image patches are represented by graph vertices with edges assigned according to the forensic similarity between patches. Localized tampering introduces unique structure into this graph, which aligns with a concept called ``community structure'' in graph-theory literature. In the Forensic Similarity Graph, communities correspond to the tampered and unaltered regions in the image. As a result, forgery detection is performed by identifying whether multiple communities exist, and forgery localization is performed by partitioning these communities. We present two community detection techniques, adapted from literature, to detect and localize image forgeries. We experimentally show that our proposed community detection methods outperform existing state-of-the-art forgery detection and localization methods, which do not capture such community structure.Comment: 16 pages, under review at IEEE Journal of Selected Topics in Signal Processin

Attacking Image Splicing Detection and Localization Algorithms Using Synthetic Traces

Recent advances in deep learning have enabled forensics researchers to develop a new class of image splicing detection and localization algorithms. These algorithms identify spliced content by detecting localized inconsistencies in forensic traces using Siamese neural networks, either explicitly during analysis or implicitly during training. At the same time, deep learning has enabled new forms of anti-forensic attacks, such as adversarial examples and generative adversarial network (GAN) based attacks. Thus far, however, no anti-forensic attack has been demonstrated against image splicing detection and localization algorithms. In this paper, we propose a new GAN-based anti-forensic attack that is able to fool state-of-the-art splicing detection and localization algorithms such as EXIF-Net, Noiseprint, and Forensic Similarity Graphs. This attack operates by adversarially training an anti-forensic generator against a set of Siamese neural networks so that it is able to create synthetic forensic traces. Under analysis, these synthetic traces appear authentic and are self-consistent throughout an image. Through a series of experiments, we demonstrate that our attack is capable of fooling forensic splicing detection and localization algorithms without introducing visually detectable artifacts into an attacked image. Additionally, we demonstrate that our attack outperforms existing alternative attack approaches.

Role of critical spin fluctuations in ultrafast demagnetization of transition-metal rare-earth alloys

Ultrafast magnetization dynamics induced by femtosecond laser pulses have been measured in ferrimagnetic Co0.8Gd0.2, Co.74Tb.26 and Co.86Tb.14 alloys. Using element sensitivity of X-ray magnetic circular dichroism at the Co L3, Tb M5 and Gd M5 edges we evidence that the demagnetization dynamics is element dependent. We show that a thermalization time as fast as 280 fs is observed for the rare-earth in the alloy, when the laser excited state temperature is below the compensation temperature. It is limited to 500 fs when the laser excited state temperature is below the Curie temperature (Tc). We propose critical spin fluctuations in the vicinity of TC as the mechanism which reduces the demagnetization rates of the 4f electrons in transition-metal rare-earth alloys whereas at any different temperature the limited demagnetization rates could be avoided.Comment: 11 pages, 4 figure

Open Set Synthetic Image Source Attribution

AI-generated images have become increasingly realistic and have garnered significant public attention. While synthetic images are intriguing due to their realism, they also pose an important misinformation threat. To address this new threat, researchers have developed multiple algorithms to detect synthetic images and identify their source generators. However, most existing source attribution techniques are designed to operate in a closed-set scenario, i.e. they can only be used to discriminate between known image generators. By contrast, new image-generation techniques are rapidly emerging. To contend with this, there is a great need for open-set source attribution techniques that can identify when synthetic images have originated from new, unseen generators. To address this problem, we propose a new metric learning-based approach. Our technique works by learning transferrable embeddings capable of discriminating between generators, even when they are not seen during training. An image is first assigned to a candidate generator, then is accepted or rejected based on its distance in the embedding space from known generators' learned reference points. Importantly, we identify that initializing our source attribution embedding network by pretraining it on image camera identification can improve our embeddings' transferability. Through a series of experiments, we demonstrate our approach's ability to attribute the source of synthetic images in open-set scenarios

VideoFACT: Detecting Video Forgeries Using Attention, Scene Context, and Forensic Traces

Fake videos represent an important misinformation threat. While existing forensic networks have demonstrated strong performance on image forgeries, recent results reported on the Adobe VideoSham dataset show that these networks fail to identify fake content in videos. In this paper, we show that this is due to video coding, which introduces local variation into forensic traces. In response, we propose VideoFACT - a new network that is able to detect and localize a wide variety of video forgeries and manipulations. To overcome challenges that existing networks face when analyzing videos, our network utilizes both forensic embeddings to capture traces left by manipulation, context embeddings to control for variation in forensic traces introduced by video coding, and a deep self-attention mechanism to estimate the quality and relative importance of local forensic embeddings. We create several new video forgery datasets and use these, along with publicly available data, to experimentally evaluate our network's performance. These results show that our proposed network is able to identify a diverse set of video forgeries, including those not encountered during training. Furthermore, we show that our network can be fine-tuned to achieve even stronger performance on challenging AI-based manipulations

Möglichkeiten zur Attraktivitätssteigerung der Hausarztmedizin aus der Sicht junger Ärztinnen und Ärzte

Hintergrund: Ziel der Studie ist es zu untersuchen, welche berufliche Laufbahn junge Ärztinnen und Ärzte gegen Ende ihrer fachärztlichen Weiterbildung anstreben und welche Faktoren aus ihrer Sicht eine Tätigkeit in der ärztlichen Grundversorgung attraktiver machen könnten. Methodik: Im Rahmen einer seit 2001 laufenden Schweizer prospektiven Studie zu Determinanten der Karriereentwicklung nahmen 534 junge Ärztinnen und Ärzte im Jahr 2007 an der vierten Befragung teil. Sie machten Angaben zur angestrebten beruflichen Laufbahn, zum geplanten Praxismodell und Praxisstandort, ferner benannten sie Faktoren, die für bzw. gegen die Hausarztmedizin sprechen und welche die Attraktivität der Hausarztmedizin steigern würden. Ergebnisse: 84 Personen (42% Männer, 58% Frauen) streben eine Tätigkeit als Hausarzt/-ärztin an (60% spezialisieren sich in Allgemeinmedizin, 40% in Allgemeiner Innerer Medizin), 450 spezialisieren sich in einem anderen Fachgebiet. Von den 534 Studienteilnehmenden möchten 208 später in einer Praxis arbeiten, mehrheitlich in einer Gruppenpraxis (88%). 49% der zukünftigen Hausärzte planen eine Praxis in einer Stadt, von den Spezialisten sind es 77%. Als wesentliche Gründe gegen die Hausarztmedizin werden die unsichere Entwicklung der Hausarztmedizin und das niedrige Einkommen genannt, als positive Faktoren die Vielseitigkeit, das breite Patientenspektrum sowie die Kontinuität der Arzt-Patient-Beziehung. Um die Attraktivität der Hausarztmedizin zu steigern, müssten interdisziplinäre Gruppenpraxen gefördert und die finanziellen Rahmenbedingungen verbessert werden. Schlussfolgerung: Die Hausarztmedizin wird als interessantes Berufsfeld eingeschätzt, die gegenwärtigen Rahmenbedingungen für die Ausübung der hausärztlichen Tätigkeit wirken jedoch abschreckend

Mesenchymal Stem Cells and Inflammatory Cardiomyopathy: Cardiac Homing and Beyond

Under conventional heart failure therapy, inflammatory cardiomyopathy usually has a progressive course, merging for alternative interventional strategies. There is accumulating support for the application of cellular transplantation as a strategy to improve myocardial function. Mesenchymal stem cells (MSCs) have the advantage over other stem cells that they possess immunomodulatory features, making them attractive candidates for the treatment of inflammatory cardiomyopathy. Studies in experimental models of inflammatory cardiomyopathy have consistently demonstrated the potential of MSCs to reduce cardiac injury and to improve cardiac function. This paper gives an overview about how inflammation triggers the functionality of MSCs and how it induces cardiac homing. Finally, the potential of intravenous application of MSCs by inflammatory cardiomyopathy is discussed