Providing detection strategies to improve human detection of deepfakes: An experimental study

Deepfake videos are becoming more pervasive. In this preregistered online experiment, participants (N = 454, M age = 37.19, SD age = 13.25, males = 57.5%) categorize a series of 20 videos as either real or deepfake. All participants saw 10 real and 10 deepfake videos. Par...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Somoray, Klaire [verfasserIn] Miller, Dan J. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Deepfake Human detection Deepfake detection strategies Accuracy Confidence

Übergeordnetes Werk:	Enthalten in: Computers in human behavior - Amsterdam [u.a.] : Elsevier Science, 1985, 149
Übergeordnetes Werk:	volume:149

DOI / URN:	10.1016/j.chb.2023.107917

Katalog-ID:	ELV064990796

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520			\|a Deepfake videos are becoming more pervasive. In this preregistered online experiment, participants (N = 454, M age = 37.19, SD age = 13.25, males = 57.5%) categorize a series of 20 videos as either real or deepfake. All participants saw 10 real and 10 deepfake videos. Participants were randomly assigned to receive a list of strategies for detecting deepfakes based on visual cues (e.g., looking for common artifacts such as skin smoothness) or to act as a control group. Participants were also asked how confident they were that they categorized each video correctly (per video confidence) and to estimate how many videos they correctly categorized out of 20 (overall confidence). The sample performed above chance on the detection activity, correctly categorizing 60.70% of videos on average (SD = 13.00). The detection strategies intervention did not impact detection accuracy or detection confidence, with the intervention and control groups performing similarly on the detection activity and showing similar levels of confidence. Inconsistent with previous research, the study did not find that participants had a bias toward categorizing videos as real. Participants overestimated their ability to detect deepfakes at the individual video level. However, they tended to underestimate their abilities on the overall confidence question.
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650		4	\|a Confidence
700	1		\|a Miller, Dan J. \|e verfasserin \|0 (orcid)0000-0002-3230-2631 \|4 aut
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allfields	10.1016/j.chb.2023.107917 doi (DE-627)ELV064990796 (ELSEVIER)S0747-5632(23)00268-6 DE-627 ger DE-627 rda eng 004 150 300 VZ Somoray, Klaire verfasserin (orcid)0000-0001-7521-1425 aut Providing detection strategies to improve human detection of deepfakes: An experimental study 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Deepfake videos are becoming more pervasive. In this preregistered online experiment, participants (N = 454, M age = 37.19, SD age = 13.25, males = 57.5%) categorize a series of 20 videos as either real or deepfake. All participants saw 10 real and 10 deepfake videos. Participants were randomly assigned to receive a list of strategies for detecting deepfakes based on visual cues (e.g., looking for common artifacts such as skin smoothness) or to act as a control group. Participants were also asked how confident they were that they categorized each video correctly (per video confidence) and to estimate how many videos they correctly categorized out of 20 (overall confidence). The sample performed above chance on the detection activity, correctly categorizing 60.70% of videos on average (SD = 13.00). The detection strategies intervention did not impact detection accuracy or detection confidence, with the intervention and control groups performing similarly on the detection activity and showing similar levels of confidence. Inconsistent with previous research, the study did not find that participants had a bias toward categorizing videos as real. Participants overestimated their ability to detect deepfakes at the individual video level. However, they tended to underestimate their abilities on the overall confidence question. Deepfake Human detection Deepfake detection strategies Accuracy Confidence Miller, Dan J. verfasserin (orcid)0000-0002-3230-2631 aut Enthalten in Computers in human behavior Amsterdam [u.a.] : Elsevier Science, 1985 149 Online-Ressource (DE-627)319508544 (DE-600)2001911-7 (DE-576)259271136 0747-5632 nnns volume:149 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 149
spelling	10.1016/j.chb.2023.107917 doi (DE-627)ELV064990796 (ELSEVIER)S0747-5632(23)00268-6 DE-627 ger DE-627 rda eng 004 150 300 VZ Somoray, Klaire verfasserin (orcid)0000-0001-7521-1425 aut Providing detection strategies to improve human detection of deepfakes: An experimental study 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Deepfake videos are becoming more pervasive. In this preregistered online experiment, participants (N = 454, M age = 37.19, SD age = 13.25, males = 57.5%) categorize a series of 20 videos as either real or deepfake. All participants saw 10 real and 10 deepfake videos. Participants were randomly assigned to receive a list of strategies for detecting deepfakes based on visual cues (e.g., looking for common artifacts such as skin smoothness) or to act as a control group. Participants were also asked how confident they were that they categorized each video correctly (per video confidence) and to estimate how many videos they correctly categorized out of 20 (overall confidence). The sample performed above chance on the detection activity, correctly categorizing 60.70% of videos on average (SD = 13.00). The detection strategies intervention did not impact detection accuracy or detection confidence, with the intervention and control groups performing similarly on the detection activity and showing similar levels of confidence. Inconsistent with previous research, the study did not find that participants had a bias toward categorizing videos as real. Participants overestimated their ability to detect deepfakes at the individual video level. However, they tended to underestimate their abilities on the overall confidence question. Deepfake Human detection Deepfake detection strategies Accuracy Confidence Miller, Dan J. verfasserin (orcid)0000-0002-3230-2631 aut Enthalten in Computers in human behavior Amsterdam [u.a.] : Elsevier Science, 1985 149 Online-Ressource (DE-627)319508544 (DE-600)2001911-7 (DE-576)259271136 0747-5632 nnns volume:149 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 149
allfields_unstemmed	10.1016/j.chb.2023.107917 doi (DE-627)ELV064990796 (ELSEVIER)S0747-5632(23)00268-6 DE-627 ger DE-627 rda eng 004 150 300 VZ Somoray, Klaire verfasserin (orcid)0000-0001-7521-1425 aut Providing detection strategies to improve human detection of deepfakes: An experimental study 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Deepfake videos are becoming more pervasive. In this preregistered online experiment, participants (N = 454, M age = 37.19, SD age = 13.25, males = 57.5%) categorize a series of 20 videos as either real or deepfake. All participants saw 10 real and 10 deepfake videos. Participants were randomly assigned to receive a list of strategies for detecting deepfakes based on visual cues (e.g., looking for common artifacts such as skin smoothness) or to act as a control group. Participants were also asked how confident they were that they categorized each video correctly (per video confidence) and to estimate how many videos they correctly categorized out of 20 (overall confidence). The sample performed above chance on the detection activity, correctly categorizing 60.70% of videos on average (SD = 13.00). The detection strategies intervention did not impact detection accuracy or detection confidence, with the intervention and control groups performing similarly on the detection activity and showing similar levels of confidence. Inconsistent with previous research, the study did not find that participants had a bias toward categorizing videos as real. Participants overestimated their ability to detect deepfakes at the individual video level. However, they tended to underestimate their abilities on the overall confidence question. Deepfake Human detection Deepfake detection strategies Accuracy Confidence Miller, Dan J. verfasserin (orcid)0000-0002-3230-2631 aut Enthalten in Computers in human behavior Amsterdam [u.a.] : Elsevier Science, 1985 149 Online-Ressource (DE-627)319508544 (DE-600)2001911-7 (DE-576)259271136 0747-5632 nnns volume:149 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 149
allfieldsGer	10.1016/j.chb.2023.107917 doi (DE-627)ELV064990796 (ELSEVIER)S0747-5632(23)00268-6 DE-627 ger DE-627 rda eng 004 150 300 VZ Somoray, Klaire verfasserin (orcid)0000-0001-7521-1425 aut Providing detection strategies to improve human detection of deepfakes: An experimental study 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Deepfake videos are becoming more pervasive. In this preregistered online experiment, participants (N = 454, M age = 37.19, SD age = 13.25, males = 57.5%) categorize a series of 20 videos as either real or deepfake. All participants saw 10 real and 10 deepfake videos. Participants were randomly assigned to receive a list of strategies for detecting deepfakes based on visual cues (e.g., looking for common artifacts such as skin smoothness) or to act as a control group. Participants were also asked how confident they were that they categorized each video correctly (per video confidence) and to estimate how many videos they correctly categorized out of 20 (overall confidence). The sample performed above chance on the detection activity, correctly categorizing 60.70% of videos on average (SD = 13.00). The detection strategies intervention did not impact detection accuracy or detection confidence, with the intervention and control groups performing similarly on the detection activity and showing similar levels of confidence. Inconsistent with previous research, the study did not find that participants had a bias toward categorizing videos as real. Participants overestimated their ability to detect deepfakes at the individual video level. However, they tended to underestimate their abilities on the overall confidence question. Deepfake Human detection Deepfake detection strategies Accuracy Confidence Miller, Dan J. verfasserin (orcid)0000-0002-3230-2631 aut Enthalten in Computers in human behavior Amsterdam [u.a.] : Elsevier Science, 1985 149 Online-Ressource (DE-627)319508544 (DE-600)2001911-7 (DE-576)259271136 0747-5632 nnns volume:149 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 149
allfieldsSound	10.1016/j.chb.2023.107917 doi (DE-627)ELV064990796 (ELSEVIER)S0747-5632(23)00268-6 DE-627 ger DE-627 rda eng 004 150 300 VZ Somoray, Klaire verfasserin (orcid)0000-0001-7521-1425 aut Providing detection strategies to improve human detection of deepfakes: An experimental study 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Deepfake videos are becoming more pervasive. In this preregistered online experiment, participants (N = 454, M age = 37.19, SD age = 13.25, males = 57.5%) categorize a series of 20 videos as either real or deepfake. All participants saw 10 real and 10 deepfake videos. Participants were randomly assigned to receive a list of strategies for detecting deepfakes based on visual cues (e.g., looking for common artifacts such as skin smoothness) or to act as a control group. Participants were also asked how confident they were that they categorized each video correctly (per video confidence) and to estimate how many videos they correctly categorized out of 20 (overall confidence). The sample performed above chance on the detection activity, correctly categorizing 60.70% of videos on average (SD = 13.00). The detection strategies intervention did not impact detection accuracy or detection confidence, with the intervention and control groups performing similarly on the detection activity and showing similar levels of confidence. Inconsistent with previous research, the study did not find that participants had a bias toward categorizing videos as real. Participants overestimated their ability to detect deepfakes at the individual video level. However, they tended to underestimate their abilities on the overall confidence question. Deepfake Human detection Deepfake detection strategies Accuracy Confidence Miller, Dan J. verfasserin (orcid)0000-0002-3230-2631 aut Enthalten in Computers in human behavior Amsterdam [u.a.] : Elsevier Science, 1985 149 Online-Ressource (DE-627)319508544 (DE-600)2001911-7 (DE-576)259271136 0747-5632 nnns volume:149 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 149
language	English
source	Enthalten in Computers in human behavior 149 volume:149
sourceStr	Enthalten in Computers in human behavior 149 volume:149
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Deepfake Human detection Deepfake detection strategies Accuracy Confidence
dewey-raw	004
isfreeaccess_bool	false
container_title	Computers in human behavior
authorswithroles_txt_mv	Somoray, Klaire @@aut@@ Miller, Dan J. @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	319508544
dewey-sort	14
id	ELV064990796
language_de	englisch
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author	Somoray, Klaire
spellingShingle	Somoray, Klaire ddc 004 misc Deepfake misc Human detection misc Deepfake detection strategies misc Accuracy misc Confidence Providing detection strategies to improve human detection of deepfakes: An experimental study
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title	Providing detection strategies to improve human detection of deepfakes: An experimental study
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title_full	Providing detection strategies to improve human detection of deepfakes: An experimental study
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title_sort	providing detection strategies to improve human detection of deepfakes: an experimental study
title_auth	Providing detection strategies to improve human detection of deepfakes: An experimental study
abstract	Deepfake videos are becoming more pervasive. In this preregistered online experiment, participants (N = 454, M age = 37.19, SD age = 13.25, males = 57.5%) categorize a series of 20 videos as either real or deepfake. All participants saw 10 real and 10 deepfake videos. Participants were randomly assigned to receive a list of strategies for detecting deepfakes based on visual cues (e.g., looking for common artifacts such as skin smoothness) or to act as a control group. Participants were also asked how confident they were that they categorized each video correctly (per video confidence) and to estimate how many videos they correctly categorized out of 20 (overall confidence). The sample performed above chance on the detection activity, correctly categorizing 60.70% of videos on average (SD = 13.00). The detection strategies intervention did not impact detection accuracy or detection confidence, with the intervention and control groups performing similarly on the detection activity and showing similar levels of confidence. Inconsistent with previous research, the study did not find that participants had a bias toward categorizing videos as real. Participants overestimated their ability to detect deepfakes at the individual video level. However, they tended to underestimate their abilities on the overall confidence question.
abstractGer	Deepfake videos are becoming more pervasive. In this preregistered online experiment, participants (N = 454, M age = 37.19, SD age = 13.25, males = 57.5%) categorize a series of 20 videos as either real or deepfake. All participants saw 10 real and 10 deepfake videos. Participants were randomly assigned to receive a list of strategies for detecting deepfakes based on visual cues (e.g., looking for common artifacts such as skin smoothness) or to act as a control group. Participants were also asked how confident they were that they categorized each video correctly (per video confidence) and to estimate how many videos they correctly categorized out of 20 (overall confidence). The sample performed above chance on the detection activity, correctly categorizing 60.70% of videos on average (SD = 13.00). The detection strategies intervention did not impact detection accuracy or detection confidence, with the intervention and control groups performing similarly on the detection activity and showing similar levels of confidence. Inconsistent with previous research, the study did not find that participants had a bias toward categorizing videos as real. Participants overestimated their ability to detect deepfakes at the individual video level. However, they tended to underestimate their abilities on the overall confidence question.
abstract_unstemmed	Deepfake videos are becoming more pervasive. In this preregistered online experiment, participants (N = 454, M age = 37.19, SD age = 13.25, males = 57.5%) categorize a series of 20 videos as either real or deepfake. All participants saw 10 real and 10 deepfake videos. Participants were randomly assigned to receive a list of strategies for detecting deepfakes based on visual cues (e.g., looking for common artifacts such as skin smoothness) or to act as a control group. Participants were also asked how confident they were that they categorized each video correctly (per video confidence) and to estimate how many videos they correctly categorized out of 20 (overall confidence). The sample performed above chance on the detection activity, correctly categorizing 60.70% of videos on average (SD = 13.00). The detection strategies intervention did not impact detection accuracy or detection confidence, with the intervention and control groups performing similarly on the detection activity and showing similar levels of confidence. Inconsistent with previous research, the study did not find that participants had a bias toward categorizing videos as real. Participants overestimated their ability to detect deepfakes at the individual video level. However, they tended to underestimate their abilities on the overall confidence question.
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title_short	Providing detection strategies to improve human detection of deepfakes: An experimental study
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doi_str	10.1016/j.chb.2023.107917
up_date	2024-07-06T21:26:31.731Z
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