Deep Fakes: Public Opinion & Effectiveness of Technology and Policies, Slides of Law

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• (^) Rithika shree. K (132202018)

A CRITICAL ANALYSIS ON ATTACKING IDENTITY

SEMANTICS IN DEEP FAKES VIA DEEP FEATURE

FUSION

METHODOLOGY

The research method followed here is descriptive research. A total 200 samples have been collected out of which all samples have been collected through convenient sampling methods. The sample frame taken here is public areas in and around Chennai. The independent variables are age, gender, educational qualification, employment status and area. The dependent variables are people's opinion towards the current technology and policies are effective in addressing the challenges posed by deep fakes, agreeability that individuals have the right to utilise deepfake technology for creative or entertainment purposes, level of importance towards addressing the issues of identity semantics in deep fakes, level of satisfaction with the current methods and technologies used to detect and combat deepfakes, level of awareness regarding the ethical and security concerns associated with deepfake technology and its potential consequences for society. The statistical tools used here are complex charts, chi-square test, independent sample t- test and correlation test.

Legend:

Figure 2 represents the area of the sample population and their effectiveness towards the current technology and policies in addressing the challenges posed by deep fakes.

Legend:

Figure 3 represents the educational qualification of the sample population and their effectiveness towards the current technology and policies in addressing the challenges posed by deep fakes.

Legend: Figure 5 represents the educational qualifiaction of the sample population and their agreeability towards the individuals having the right to utilise deep fake technology for creative or entertainment purposes.

Legend: Figure 6 represents the area of the sample population and their agreeability towards the individuals having the right to utilise deep fake technology for creative or entertainment purposes.

Legend: Figure 8 represents the area of the sample population and their level of importance regarding the issues of identity semantics in deep fakes.

Legend: Figure 9 represents the gender of the sample population and their level of importance regarding the issues of identity semantics in deep fakes.

Legend: Figure 11 represents the employment status of the sample population and their level of satisfaction with the current methods and technologies used to detect and combat deepfakes.

Legend: Figure 12 represents the area of the sample population and their level of satisfaction with the current methods and technologies used to detect and combat deepfakes.

Table 2 Legend: Table 2 uses the independent sample t-test to show the difference between the gender groups and the current technology and policies are effective in addressing the challenges posed by deep fakes.

Table 3 Legend: Table 3 uses the chi-square test to show the association between the individuals having the right to utilise deep fake technology for creative or entertainment purposes and employment status of the respondent.