Misleading or false information has been creating chaos in some places around the world. To mitigate this issue, many researchers have proposed automated fact-checking methods to fight the spread of fake news. However, most methods cannot explain the reasoning behind their decisions, failing to build trust between machines and humans using such technology. Trust is essential for fact-checking to be applied in the real world. Here, we address fact-checking explainability through question answering. In particular, we propose generating questions and answers from claims and answering the same questions from evidence. We also propose an answer comparison model with an attention mechanism attached to each question. Leveraging question answering as a proxy, we break down automated fact-checking into several steps — this separation aids models’ explainability as it allows for more detailed analysis of their decision-making processes. Experimental results show that the proposed model can achieve state-of-the-art performance while providing reasonable explainable capabilities.
@inproceedings{yang2021explainable,title={Explainable Fact-checking through Question Answering},author={Yang, Jing and Vega-Oliveros, Didier and Seibt, Ta{\'\i}s and Rocha, Anderson},abbr={ICASSP},bibtex_show={true},booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},year={2022},organization={IEEE},pdf={icassp2020_explainable.pdf},code={https://github.com/jingyng/explainable-fact-checking},selected={true}}
2021
WIFS
Scalable Fact-checking with Human-in-the-Loop
Yang, Jing, Vega-Oliveros, Didier, Seibt, Tais, and Rocha, Anderson
In 2021 IEEE International Workshop on Information Forensics and Security (WIFS) 2021
Researchers have been investigating automated solutions for fact-checking in various fronts. However, current approaches often overlook the fact that information released every day is escalating, and a large amount of them overlap. Intending to accelerate fact-checking, we bridge this gap by proposing a new pipeline – grouping similar messages and summarizing them into aggregated claims. Specifically, we first clean a set of social media posts (e.g., tweets) and build a graph of all posts based on their semantics; Then, we perform two clustering methods to group the messages for further claim summarization. We evaluate the summaries both quantitatively with ROUGE scores and qualitatively with human evaluation. We also generate a graph of summaries to verify that there is no significant overlap among them. The results reduced 28,818 original messages to 700 summary claims, showing the potential to speed up the fact-checking process by organizing and selecting representative claims from massive disorganized and redundant messages.
@inproceedings{yang2021scalable,title={Scalable Fact-checking with Human-in-the-Loop},author={Yang, Jing and Vega-Oliveros, Didier and Seibt, Tais and Rocha, Anderson},booktitle={2021 IEEE International Workshop on Information Forensics and Security (WIFS)},year={2021},bibtex_show={true},doi={10.1109/WIFS53200.2021.9648388},abbr={WIFS},code={https://github.com/jingyng/scalable-fact-checking},pdf={wifs2021_scalable.pdf},organization={IEEE},selected={true}}
A Inteligência Artificial e os desafios da Ciência Forense Digital no século XXI
Padilha, Rafael, Theóphilo, Antônio, Andaló, Fernanda A, Vega-Oliveros, Didier A, Cardenuto, João P, Bertocco, Gabriel, Nascimento, José,
Yang, Jing, and Rocha, Anderson
@article{padilha2021inteligencia,title={A Intelig{\^e}ncia Artificial e os desafios da Ci{\^e}ncia Forense Digital no s{\'e}culo XXI},author={Padilha, Rafael and The{\'o}philo, Ant{\^o}nio and Andal{\'o}, Fernanda A and Vega-Oliveros, Didier A and Cardenuto, Jo{\~a}o P and Bertocco, Gabriel and Nascimento, Jos{\'e} and Yang, Jing and Rocha, Anderson},bibtex_show={true},journal={Estudos Avan{\c{c}}ados},volume={35},pages={113--138},year={2021},publisher={SciELO Brasil}}
2019
Source identification of 3D printed objects based on inherent equipment distortion
Peng, Fei,
Yang, Jing, Lin, Zi-Xing, and Long, Min
The widespread use of 3D printers introduces tremendous challenges for the regulation of illegal products. In the current situation, since it is impossible to completely prohibit users from using 3D printers to manufacture illegal products, source identification of 3D printed products is a possible alternative for regulators to trace the offenders. In this paper, a source identification scheme for 3D printed objects based on inherent equipment distortion is proposed. By investigating the 3D printing process, an equipment distortion model is constructed, and then the inherent equipment distortion is analyzed. Furthermore, in order to exhibit the inherent equipment distortion, a uniform mark is designed and the inherent equipment distortion is extracted. With the features of the inherent equipment distortion of the 3D printers, SVM classifier is employed for the source identification of the 3D printed objects. Experimental results and analysis show that it can obtain an average identification accuracy of 91.1% with the 3D printed objects from 9 printers, and the analysis also indicates that it can achieve satisfactory robustness and reliability.
@article{peng2019source,title={Source identification of 3D printed objects based on inherent equipment distortion},author={Peng, Fei and Yang, Jing and Lin, Zi-Xing and Long, Min},journal={Computers \& Security},bibtex_show={true},doi={10.1016/j.cose.2018.12.015},volume={82},pages={173--183},year={2019},publisher={Elsevier}}
2018
3-D printed object authentication based on printing noise and digital signature
With the development of 3-D printing and reverse engineering, the protection of intellectual property of 3-D printed objects is becoming a prominent problem. In order to authenticate 3-D printed objects, an authentication scheme based on printing noise and digital signature is proposed. First, the noises introduced in the 3-D printing and observation are investigated. Thereafter, a special authentication mark is designed for extracting the printing noise. Based on this, a 3-D printed object authentication framework is built and it is composed of two processes-registration and verification. In the registration, the printing noise of the authentication mark is extracted and signed by digital signature. While in the verification, the signature is verified and then the printing noise of the authentication mark is extracted. After that, the extracted printing noise is matched with the one acquired in the registration. Experimental results and analysis show that the proposed scheme can reliably accomplish the authentication of the 3-D printed object with high precision and that it can achieve high security and good robustness.
@article{peng20183,title={3-D printed object authentication based on printing noise and digital signature},author={Peng, Fei and Yang, Jing and Long, Min},journal={IEEE Transactions on Reliability},bibtex_show={true},doi={10.1109/TR.2018.2869303},volume={68},number={1},pages={342--353},year={2018},publisher={IEEE}}