Identifying a queer aesthetic in amateur video manipulations of Disney content as part of a utopian visualisation of lesbian desires by Isabella Macleod

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Vehicle Speed Measurement Methodology Robust to Playback Speed-Manipulated Video File by Kyu-Sun Shim, Nam in Park, Jin-Hwan Kim, Oc-Yeub Jeon, Heejo Lee

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Sliding Adjustment for 3D Video Representation by Galpin Franck, Morin Luce

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A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning Processing by Sara Ferreira, Mário Antunes, Manuel E. Correia

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Surface tracking assessment and interaction in texture space by Johannes Furch, Anna Hilsmann, Peter Eisert

“…Abstract In this paper, we present a novel approach for assessing and interacting with surface tracking algorithms targeting video manipulation in post-production. As tracking inaccuracies are unavoidable, we enable the user to provide small hints to the algorithms instead of correcting erroneous results afterwards. …”
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Chroma key background detection for digital video using statistical correlation of blurring artifact by Bagiwa, M.A., Wahab, A.W.A., Idris, M.Y.I., Khan, S., Choo, K.K.R.

“…However, detecting such video manipulation is an understudied topic. Digital forgers may present a manipulated video from chroma key composition as evidence in court, thus creating a severe problem. …”

Image and video generation via deep learning by Jiang, Liming

“…The first attempt is to construct a large-scale facial video dataset, DeeperForensics-1.0, to facilitate the following research and prevent the negative impact of generated data via better video manipulation. After securing the countermeasures, a versatile Two-Stream Image-to-image Translation (TSIT) framework is proposed, which has high practical value. …”
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Focusing on Future Consequences Enhances Self-Controlled Dietary Choices by Johanna Kruse, Franziska M. Korb, Caroline Surrey, Uta Wolfensteller, Thomas Goschke, Stefan Scherbaum

“…Instruction to focus on long-term consequences compared to short-term consequences increased the number of healthy choices, reduced response times for healthy decisions, and increased the influence of health aspects during the decision-making process. The effect of video manipulation showed greater variability. While focusing on long-term consequences facilitated healthy food choices and reduced the underlying decision conflict, the current mindset appeared to have a minor influence.…”
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Frame Identification of Object-Based Video Tampering Using Symmetrically Overlapped Motion Residual by Tae Hyung Kim, Cheol Woo Park, Il Kyu Eom

“…Image and video manipulation has been actively used in recent years with the development of multimedia editing technologies. …”
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Effects on Long-Range Dependence and Multifractality in Temporal Resolution Recovery of High Frame Rate HEVC Compressed Content by Ana Gavrovska

“…The obtained results show the effects on LRD and multifractality and their significance in understanding changes in typical video manipulation. The proposed model can be valuable in video credibility and quality assessments of HFR HEVC compressed content.…”
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Information Accrual From the Period Preceding Racket-Ball Contact for Tennis Ground Strokes: Inferences From Stochastic Masking by Sepehr Jalali, Sian E. Martin, Tandra Ghose, Richard M. Buscombe, Joshua A. Solomon, Kielan Yarrow

“…This result is broadly consistent with prior work using nonstochastic approaches to video manipulation, and cannot be an artifact of temporal smear from information accrued after racket-ball contact, because no such information was present.…”
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Deep Convolutional Neural Network for Robust Detection of Object-Based Forgeries in Advanced Video by Ahmad A. Mazhar, Abid Jameel, Mohammad Nadeem, Mohammad Asmatullah Khan, Jawad Hasan Alkhateeb, Faiza Bibi, Ali Mohammad Seerat

“…This paper not only contributes to the field of video forgery detection but also underscores the potential of deep learning, particularly DCNN, in addressing the evolving challenges of digital video manipulation. The findings open avenues for future research in the localization of forged regions and the application of DCNN in lower bitrate or lower resolution video sequences.…”
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Spotting Deepfakes and Face Manipulations by Fusing Features from Multi-Stream CNNs Models by Semih Yavuzkilic, Abdulkadir Sengur, Zahid Akhtar, Kamran Siddique

“…Deepfakes are a sort of image or video manipulation in which a person’s image is changed or swapped with that of another person’s face using artificial neural networks. …”
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Promoting Vaccination in India through Videos: The Role of Humor, Collectivistic Appeal and Gender by Amelia M. Jamison, Rajiv N. Rimal, Rohini Ganjoo, Julia Burleson, Neil Alperstein, Ananya Bhaktaram, Paola Pascual-Ferra, Satyanarayan Mohanty, Manoj Parida, Sidharth Rath, Eleanor Kluegel, Peter Z. Orton, Daniel J. Barnett

“…Beliefs about vaccines and those about vaccination were obtained before and after viewing the video. Manipulation checks demonstrated that each of the three independent variables was manipulated successfully. …”
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Real Life Scenes to Study Emotions in Women by Ana Carolina Aguiar-Moreira, Thiago Maehara Pereira Pinho, Gabriela Campos Oliveira Filgueira, Rosa Wanda Diez-Garcia

“…The methodology adopted herein combined qualitative and quantitative methods and involved three distinct study stages: (i) use of a descriptive question to identify daily situations that evoked negative emotions among women; (ii) construction of everyday life scenes; (iii) assessment of how effectively the constructed videos manipulated emotions. The visual analog scale (VAS), a descriptive question, and a focus group were used to discuss the negative emotion and neutral emotional condition evoked during the experiment. …”
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Layered neural rendering for retiming people in video by Lu, E, Cole, F, Dekel, T, Xie, W, Zisserman, A, Salesin, D, Freeman, WT, Rubinstein, M

“…We present a method for retiming people in an ordinary, natural video --- manipulating and editing the time in which different motions of individuals in the video occur. …”

Deepfakes on Twitter: Which Actors Control Their Spread? by Jesús Pérez Dasilva, Koldobika Meso Ayerdi, Terese Mendiguren Galdospin

“…The term deepfake was first used in a Reddit post in 2017 to refer to videos manipulated using artificial intelligence techniques and since then it is becoming easier to create such fake videos. …”
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Deepfake Video Detection using Neural Networks by Patel Nimitt, Jethwa Niket, Mali Chirag, Deone Jyoti

“…In today’s era, software tools based on deep learning have made the people work easier to make credible faces exchanges in video with little signs of manipulation, nicknamed “DeepFake” videos. Manipulation in digital media has been performed for decades through the appropriate use of visual effects; nevertheless, current breakthroughs occurred in deep learning have resulted in a significant rise to gain reality of fake material or contents using the simple ways. …”
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Catechol-O-Methyltransferase moderates effect of stress mindset on affect and cognition. by Alia J Crum, Modupe Akinola, Bradley P Turnwald, Ted J Kaptchuk, Kathryn T Hall

“…The associations of the COMT rs4680 polymorphism with the effect of stress mindset video manipulations on cognitive and affective responses were examined. …”
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Media Forensics Considerations on DeepFake Detection with Hand-Crafted Features by Dennis Siegel, Christian Kraetzer, Stefan Seidlitz, Jana Dittmann

“…DeepFake detection is a novel task for media forensics and is currently receiving a lot of research attention due to the threat these targeted video manipulations propose to the trust placed in video footage. …”
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