Poster

Sohrawardi, Saniat Javid; Chintha, Akash; Thai, Bao; Seng, Sovantharith; Hickerson, Andrea; Ptucha, Raymond; Wright, Matthew

doi:10.1145/3319535.3363269

Cited by 32 publications

(3 citation statements)

References 7 publications

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“…In the Table 1 (see Annex) we summarize the similarities and differences between fake videos and deepfakes (Sohrawardi et al, 2019;Shilma et al, 2023;Haseena et al, 2023;Matthews, 2023).…”

Section: Detecting Fake Videos and Deep Fakesmentioning

confidence: 99%

Impact of Artificial Intelligence on RTVE: Verification of fake videos and Deep fakes, content generation, and new professional profiles

Sánchez-Esparza,

Palella-Stracuzzi,

Fernández-Fernández

2024

Communication & Society

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The emergence of technologies based on artificial intelligence is accelerating the digital transformation of media organizations, directly impacting work processes, the relationship with audiences, the generation of content, and the emergence of new professional profiles. It is also, and notably, transforming the processes of detecting and verifying false content. This descriptive-exploratory research analyzes the impact that the use of AI is having on the transformation of the public entity Radiotelevisión Española (RTVE). Through a literature review and interviews with RTVE executives and experts, it reveals the transformative impact of AI on the corporation, highlighting its use to generate new content and verify the authenticity of fake and deepfake videos. In this area, RTVE combines traditional methodologies with others based on AI and leads the development of several tools in collaboration with several universities. These tools have already yielded satisfactory results in detecting these misleading materials, reinforcing RTVE’s role as a guarantor of the veracity of information and increasing citizens’ trust in its content. Similarly, AI is reinforcing RTVE’s identity as a public service by facilitating the generation of automated content, which guarantees access to information in depopulated territories, and others that connect new generations with cultural content. The arrival of artificial intelligence will also generate in a short time a transformation of profiles and professional roles that adapt to this new reality.

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“…In the Table 1 (see Annex) we summarize the similarities and differences between fake videos and deepfakes (Sohrawardi et al, 2019;Shilma et al, 2023;Haseena et al, 2023;Matthews, 2023).…”

Section: Detecting Fake Videos and Deep Fakesmentioning

confidence: 99%

Impact of Artificial Intelligence on RTVE: Verification of fake videos and Deep fakes, content generation, and new professional profiles

Sánchez-Esparza,

Palella-Stracuzzi,

Fernández-Fernández

2024

Communication & Society

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“…Numerous RNN models have been found to be employed in the creation and detection of deepfake images and videos. Here are several RNN models that can be used to create and detect deepfakes: BiLSTM [94], FaceNet, FacenetLSTM [62], Neural-ODE [95], CLRNet [96,97], CNN+(Bidirectional+entropy RNN) [98], CNN+RNN [99].…”

Section: Rnn-based Modelsmentioning

confidence: 99%

An Investigation of the Effectiveness of Deepfake Models and Tools

Mukta

Ahmad

Raiaan

et al. 2023

JSAN

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With the development of computer vision and deep learning technologies, rapidly expanding approaches have been introduced that allow anyone to create videos and pictures that are both phony and incredibly lifelike. The term deepfake methodology is used to describe such technologies. Face alteration can be performed both in videos and pictures with extreme realism using deepfake innovation. Deepfake recordings, the majority of them targeting politicians or celebrity personalities, have been widely disseminated online. On the other hand, different strategies have been outlined in the research to combat the issues brought up by deepfake. In this paper, we carry out a review by analyzing and comparing (1) the notable research contributions in the field of deepfake models and (2) widely used deepfake tools. We have also built two separate taxonomies for deepfake models and tools. These models and tools are also compared in terms of underlying algorithms, datasets they have used and their accuracy. A number of challenges and open issues have also been identified.

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“…[ 42 ] propose a deepfake detection system aimed at journalists. The authors choose a video detection model and construct their own temporal based detec-tion model including frame-level artifacts, inspired by an RNN-based deepfake detection model.…”

Section: Deepfakesmentioning

confidence: 99%