scholarly journals Detection of Manipulated Face Videos over Social Networks: A Large-Scale Study

2021 ◽  
Vol 7 (10) ◽  
pp. 193
Author(s):  
Federico Marcon ◽  
Cecilia Pasquini ◽  
Giulia Boato
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Forensic Analysis ◽  
General Purpose ◽  
Fine Tuning ◽  
Specific Technique ◽  
Multimedia Forensics ◽  
Shared Data ◽  
Social Media Platforms ◽  
The Web

The detection of manipulated videos represents a highly relevant problem in multimedia forensics, which has been widely investigated in the last years. However, a common trait of published studies is the fact that the forensic analysis is typically applied on data prior to their potential dissemination over the web. This work addresses the challenging scenario where manipulated videos are first shared through social media platforms and then are subject to the forensic analysis. In this context, a large scale performance evaluation has been carried out involving general purpose deep networks and state-of-the-art manipulated data, and studying different effects. Results confirm that a performance drop is observed in every case when unseen shared data are tested by networks trained on non-shared data; however, fine-tuning operations can mitigate this problem. Also, we show that the output of differently trained networks can carry useful forensic information for the identification of the specific technique used for visual manipulation, both for shared and non-shared data.

scholarly journals VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents

2021 ◽  
Vol 7 (3) ◽  
pp. 50
Author(s):  
Anselmo Ferreira ◽  
Ehsan Nowroozi ◽  
Mauro Barni
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Child Pornography ◽  
Forensic Analysis ◽  
Synthetic Image ◽  
Image Detection ◽  
Face Images ◽  
Large Scale Dataset ◽  
Scanned Images ◽  
Analysis Of The Images

The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.

Unsupervised acquisition of entailment relations from the Web

2013 ◽  
Vol 21 (1) ◽  
pp. 3-47 ◽  
Author(s):  
IDAN SZPEKTOR ◽  
HRISTO TANEV ◽  
IDO DAGAN ◽  
BONAVENTURA COPPOLA ◽  
MILEN KOUYLEKOV
Keyword(s):  
Large Scale ◽  
Learning Algorithm ◽  
State Of The Art ◽  
High Coverage ◽  
Web Based ◽  
Rule Acquisition ◽  
Learning Rules ◽  
Entailment Relation ◽  
Entailment Relations ◽  
The Web

AbstractEntailment recognition is a primary generic task in natural language inference, whose focus is to detect whether the meaning of one expression can be inferred from the meaning of the other. Accordingly, many NLP applications would benefit from high coverage knowledgebases of paraphrases and entailment rules. To this end, learning such knowledgebases from the Web is especially appealing due to its huge size as well as its highly heterogeneous content, allowing for a more scalable rule extraction of various domains. However, the scalability of state-of-the-art entailment rule acquisition approaches from the Web is still limited. We present a fully unsupervised learning algorithm for Web-based extraction of entailment relations. We focus on increased scalability and generality with respect to prior work, with the potential of a large-scale Web-based knowledgebase. Our algorithm takes as its input a lexical–syntactic template and searches the Web for syntactic templates that participate in an entailment relation with the input template. Experiments show promising results, achieving performance similar to a state-of-the-art unsupervised algorithm, operating over an offline corpus, but with the benefit of learning rules for different domains with no additional effort.

scholarly journals Unsupervised online multitask learning of behavioral sentence embeddings

2019 ◽  
Vol 5 ◽  
pp. e200
Author(s):  
Shao-Yen Tseng ◽  
Brian Baucom ◽  
Panayiotis Georgiou
Keyword(s):  
Emotion Recognition ◽  
Behavior Analysis ◽  
Couples Therapy ◽  
Large Scale ◽  
State Of The Art ◽  
General Purpose ◽  
Multitask Learning ◽  
Multiple Tasks ◽  
And Behavior ◽  
Better Than

Appropriate embedding transformation of sentences can aid in downstream tasks such as NLP and emotion and behavior analysis. Such efforts evolved from word vectors which were trained in an unsupervised manner using large-scale corpora. Recent research, however, has shown that sentence embeddings trained using in-domain data or supervised techniques, often through multitask learning, perform better than unsupervised ones. Representations have also been shown to be applicable in multiple tasks, especially when training incorporates multiple information sources. In this work we aspire to combine the simplicity of using abundant unsupervised data with transfer learning by introducing an online multitask objective. We present a multitask paradigm for unsupervised learning of sentence embeddings which simultaneously addresses domain adaption. We show that embeddings generated through this process increase performance in subsequent domain-relevant tasks. We evaluate on the affective tasks of emotion recognition and behavior analysis and compare our results with state-of-the-art general-purpose supervised sentence embeddings. Our unsupervised sentence embeddings outperform the alternative universal embeddings in both identifying behaviors within couples therapy and in emotion recognition.

scholarly journals Hybrid reference-based Video Source Identification

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 649 ◽  
Author(s):  
Massimo Iuliani ◽  
Marco Fontani ◽  
Dasara Shullani ◽  
Alessandro Piva
Keyword(s):  
Social Media ◽  
Source Identification ◽  
State Of The Art ◽  
Visual Media ◽  
Multimedia Forensics ◽  
Reference Pattern ◽  
Still Images ◽  
Video Source ◽  
Video Frames ◽  
Social Media Platforms

Millions of users share images and videos generated by mobile devices with different profiles on social media platforms. When publishing illegal content, they prefer to use anonymous profiles. Multimedia Forensics allows us to determine whether videos or images have been captured with the same device, and thus, possibly, by the same person. Currently, the most promising technology to achieve this task exploits unique traces left by the camera sensor into the visual content. However, image and video source identification are still treated separately from one another. This approach is limited and anachronistic, if we consider that most of the visual media are today acquired using smartphones that capture both images and videos. In this paper we overcome this limitation by exploring a new approach that synergistically exploits images and videos to study the device from which they both come. Indeed, we prove it is possible to identify the source of a digital video by exploiting a reference sensor pattern noise generated from still images taken by the same device. The proposed method provides performance comparable with or even better than the state-of-the-art, where a reference pattern is estimated from video frames. Finally, we show that this strategy is effective even in the case of in-camera digitally stabilized videos, where a non-stabilized reference is not available, thus solving the limitations of the current state-of-the-art. We also show how this approach allows us to link social media profiles containing images and videos captured by the same sensor.

scholarly journals Media forensics on social media platforms: a survey

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Cecilia Pasquini ◽  
Irene Amerini ◽  
Giulia Boato
Keyword(s):  
Social Media ◽  
Digital Media ◽  
Visual Information ◽  
Open Systems ◽  
Forensic Analysis ◽  
Provenance Analysis ◽  
Multimedia Forensics ◽  
Media Forensics ◽  
Media Objects ◽  
Social Media Platforms

AbstractThe dependability of visual information on the web and the authenticity of digital media appearing virally in social media platforms has been raising unprecedented concerns. As a result, in the last years the multimedia forensics research community pursued the ambition to scale the forensic analysis to real-world web-based open systems. This survey aims at describing the work done so far on the analysis of shared data, covering three main aspects: forensics techniques performing source identification and integrity verification on media uploaded on social networks, platform provenance analysis allowing to identify sharing platforms, and multimedia verification algorithms assessing the credibility of media objects in relation to its associated textual information. The achieved results are highlighted together with current open issues and research challenges to be addressed in order to advance the field in the next future.

TransBERT

2021 ◽  
Vol 20 (1) ◽  
pp. 1-20
Author(s):  
Zhongyang Li ◽  
Xiao Ding ◽  
Ting Liu
Keyword(s):  
Natural Language ◽  
Large Scale ◽  
State Of The Art ◽  
Language Model ◽  
Fine Tuning ◽  
Action Prediction ◽  
Target Task ◽  
Language Knowledge ◽  
Previous State ◽  
Transfer Tasks

Recent advances, such as GPT, BERT, and RoBERTa, have shown success in incorporating a pre-trained transformer language model and fine-tuning operations to improve downstream NLP systems. However, this framework still has some fundamental problems in effectively incorporating supervised knowledge from other related tasks. In this study, we investigate a transferable BERT (TransBERT) training framework, which can transfer not only general language knowledge from large-scale unlabeled data but also specific kinds of knowledge from various semantically related supervised tasks, for a target task. Particularly, we propose utilizing three kinds of transfer tasks, including natural language inference, sentiment classification, and next action prediction, to further train BERT based on a pre-trained model. This enables the model to get a better initialization for the target task. We take story-ending prediction as the target task to conduct experiments. The final results of 96.0% and 95.0% accuracy on two versions of Story Cloze Test datasets dramatically outperform previous state-of-the-art baseline methods. Several comparative experiments give some helpful suggestions on how to select transfer tasks to improve BERT. Furthermore, experiments on six English and three Chinese datasets show that TransBERT generalizes well to other tasks, languages, and pre-trained models.

scholarly journals CNN-Based Target Recognition and Identification for Infrared Imaging in Defense Systems

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2040 ◽  
Author(s):  
Antoine d’Acremont ◽  
Ronan Fablet ◽  
Alexandre Baussard ◽  
Guillaume Quin
Keyword(s):  
Large Scale ◽  
Data Augmentation ◽  
Infrared Imaging ◽  
State Of The Art ◽  
Object Identification ◽  
Fine Tuning ◽  
Support Vector ◽  
Defense Systems ◽  
Large Scale Dataset ◽  
In The Wild

Convolutional neural networks (CNNs) have rapidly become the state-of-the-art models for image classification applications. They usually require large groundtruthed datasets for training. Here, we address object identification and recognition in the wild for infrared (IR) imaging in defense applications, where no such large-scale dataset is available. With a focus on robustness issues, especially viewpoint invariance, we introduce a compact and fully convolutional CNN architecture with global average pooling. We show that this model trained from realistic simulation datasets reaches a state-of-the-art performance compared with other CNNs with no data augmentation and fine-tuning steps. We also demonstrate a significant improvement in the robustness to viewpoint changes with respect to an operational support vector machine (SVM)-based scheme.

Extremely Low-Resource Text Simplification with Pre-trained Transformer Language Model

2020 ◽  
Vol 30 (01) ◽  
pp. 2050001
Author(s):  
Takumi Maruyama ◽  
Kazuhide Yamamoto
Keyword(s):  
Machine Translation ◽  
Large Scale ◽  
State Of The Art ◽  
Language Model ◽  
Fine Tuning ◽  
Neural Machine Translation ◽  
Low Resource ◽  
Resource Setting ◽  
Text Simplification ◽  
Low Resource Setting

Inspired by machine translation task, recent text simplification approaches regard a task as a monolingual text-to-text generation, and neural machine translation models have significantly improved the performance of simplification tasks. Although such models require a large-scale parallel corpus, such corpora for text simplification are very few in number and smaller in size compared to machine translation task. Therefore, we have attempted to facilitate the training of simplification rewritings using pre-training from a large-scale monolingual corpus such as Wikipedia articles. In addition, we propose a translation language model to seamlessly conduct a fine-tuning of text simplification from the pre-training of the language model. The experimental results show that the translation language model substantially outperforms a state-of-the-art model under a low-resource setting. In addition, a pre-trained translation language model with only 3000 supervised examples can achieve a performance comparable to that of the state-of-the-art model using 30,000 supervised examples.

scholarly journals Story Ending Prediction by Transferable BERT

2019 ◽  
Author(s):  
Zhongyang Li ◽  
Xiao Ding ◽  
Ting Liu
Keyword(s):  
Natural Language ◽  
Large Scale ◽  
State Of The Art ◽  
Language Model ◽  
Fine Tuning ◽  
Action Prediction ◽  
Target Task ◽  
Language Knowledge ◽  
Previous State ◽  
Transfer Tasks

Recent advances, such as GPT and BERT, have shown success in incorporating a pre-trained transformer language model and fine-tuning operation to improve downstream NLP systems. However, this framework still has some fundamental problems in effectively incorporating supervised knowledge from other related tasks. In this study, we investigate a transferable BERT (TransBERT) training framework, which can transfer not only general language knowledge from large-scale unlabeled data but also specific kinds of knowledge from various semantically related supervised tasks, for a target task. Particularly, we propose utilizing three kinds of transfer tasks, including natural language inference, sentiment classification, and next action prediction, to further train BERT based on a pre-trained model. This enables the model to get a better initialization for the target task. We take story ending prediction as the target task to conduct experiments. The final result, an accuracy of 91.8%, dramatically outperforms previous state-of-the-art baseline methods. Several comparative experiments give some helpful suggestions on how to select transfer tasks to improve BERT.

scholarly journals Adapting Pre-trained Language Models to Rumor Detection on Twitter

2021 ◽  
Vol 27 (10) ◽  
pp. 1128-1148
Author(s):  
Hamda Slimi ◽  
Ibrahim Bounhas ◽  
Yahya Slimani
Keyword(s):  
State Of The Art ◽  
Language Model ◽  
Fine Tuning ◽  
Language Models ◽  
Fake News ◽  
Timely Manner ◽  
Fast Pace ◽  
Social Media Platforms ◽  
Rumor Detection ◽  
Malicious Intent

Fake news has invaded social media platforms where false information is being propagated with malicious intent at a fast pace. These circumstances required the development of solutions to monitor and detect rumor in a timely manner. In this paper, we propose an approach that seeks to detect emerging and unseen rumors on Twitter by adapting a pre-trained language model to the task of rumor detection, namely RoBERTa. A comparison against content-based characteristics has shown the capability of the model to surpass handcrafted features. Experimental results show that our approach outperforms state of the art ones in all metrics and that the fine tuning of RoBERTa led to richer word embeddings that consistently and significantly enhance the precision of rumor recognition.

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