Irene Amerini et al, Deepfake Video Detection Using Recurrent Neural Networks, IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
أكثر المقالات المقتبسة عن التقنيات الحسابيّة المعتمدة على الذكاء الاصطناعيّ لتحديد محتويات تمّت معالجتها بواسطة تكنولوجيا التزييف.
Abstract:
Recent advances in visual media technology have led to new tools for processing and, above all, generating multimedia contents. In particular, modern AI-based technologies have provided easy-to-use tools to create extremely realistic manipulated videos. Such synthetic videos, named Deep Fakes, may constitute a serious threat to attack the reputation of public subjects or to address the general opinion on a certain event. According to this, being able to individuate this kind of fake information becomes fundamental. In this work, a new forensic technique able to discern between fake and original video sequences is given; unlike other state-of-the-art methods which resorts at single video frames, we propose the adoption of optical flow fields to exploit possible inter-frame dissimilarities. Such a clue is then used as feature to be learned by CNN classifiers. Preliminary results obtained on FaceForensics++ dataset highlight very promising performances.
رابط للمقال: https://ieeexplore.ieee.org/document/9022558
