Temporal shuffling for defending deep action recognition models against adversarial attacks

Recently, video-based action recognition methods using convolutional neural networks (CNNs) achieve remarkable recognition performance. However, there is still lack of understanding about the generalization mechanism of action recognition models. In this paper, we suggest that action recognition mod...
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Gespeichert in:

Autor*in:	Hwang, Jaehui [verfasserIn] Zhang, Huan [verfasserIn] Choi, Jun-Ho [verfasserIn] Hsieh, Cho-Jui [verfasserIn] Lee, Jong-Seok [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Adversarial attack/defense Action recognition Temporal information in action recognition

Übergeordnetes Werk:	Enthalten in: Neural networks - Amsterdam : Elsevier, 1988, 169, Seite 388-397
Übergeordnetes Werk:	volume:169 ; pages:388-397

DOI / URN:	10.1016/j.neunet.2023.10.033

Katalog-ID:	ELV066194377

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520			\|a Recently, video-based action recognition methods using convolutional neural networks (CNNs) achieve remarkable recognition performance. However, there is still lack of understanding about the generalization mechanism of action recognition models. In this paper, we suggest that action recognition models rely on the motion information less than expected, and thus they are robust to randomization of frame orders. Furthermore, we find that motion monotonicity remaining after randomization also contributes to such robustness. Based on this observation, we develop a novel defense method using temporal shuffling of input videos against adversarial attacks for action recognition models. Another observation enabling our defense method is that adversarial perturbations on videos are sensitive to temporal destruction. To the best of our knowledge, this is the first attempt to design a defense method without additional training for 3D CNN-based video action recognition models.
650		4	\|a Adversarial attack/defense
650		4	\|a Action recognition
650		4	\|a Temporal information in action recognition
700	1		\|a Zhang, Huan \|e verfasserin \|4 aut
700	1		\|a Choi, Jun-Ho \|e verfasserin \|4 aut
700	1		\|a Hsieh, Cho-Jui \|e verfasserin \|4 aut
700	1		\|a Lee, Jong-Seok \|e verfasserin \|0 (orcid)0000-0002-8038-1119 \|4 aut
773	0	8	\|i Enthalten in \|t Neural networks \|d Amsterdam : Elsevier, 1988 \|g 169, Seite 388-397 \|h Online-Ressource \|w (DE-627)302468536 \|w (DE-600)1491372-0 \|w (DE-576)07971997X \|x 1879-2782 \|7 nnns
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publishDate	2023
allfields	10.1016/j.neunet.2023.10.033 doi (DE-627)ELV066194377 (ELSEVIER)S0893-6080(23)00588-9 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Hwang, Jaehui verfasserin (orcid)0009-0003-7785-6886 aut Temporal shuffling for defending deep action recognition models against adversarial attacks 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, video-based action recognition methods using convolutional neural networks (CNNs) achieve remarkable recognition performance. However, there is still lack of understanding about the generalization mechanism of action recognition models. In this paper, we suggest that action recognition models rely on the motion information less than expected, and thus they are robust to randomization of frame orders. Furthermore, we find that motion monotonicity remaining after randomization also contributes to such robustness. Based on this observation, we develop a novel defense method using temporal shuffling of input videos against adversarial attacks for action recognition models. Another observation enabling our defense method is that adversarial perturbations on videos are sensitive to temporal destruction. To the best of our knowledge, this is the first attempt to design a defense method without additional training for 3D CNN-based video action recognition models. Adversarial attack/defense Action recognition Temporal information in action recognition Zhang, Huan verfasserin aut Choi, Jun-Ho verfasserin aut Hsieh, Cho-Jui verfasserin aut Lee, Jong-Seok verfasserin (orcid)0000-0002-8038-1119 aut Enthalten in Neural networks Amsterdam : Elsevier, 1988 169, Seite 388-397 Online-Ressource (DE-627)302468536 (DE-600)1491372-0 (DE-576)07971997X 1879-2782 nnns volume:169 pages:388-397 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 54.72 Künstliche Intelligenz VZ AR 169 388-397
spelling	10.1016/j.neunet.2023.10.033 doi (DE-627)ELV066194377 (ELSEVIER)S0893-6080(23)00588-9 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Hwang, Jaehui verfasserin (orcid)0009-0003-7785-6886 aut Temporal shuffling for defending deep action recognition models against adversarial attacks 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, video-based action recognition methods using convolutional neural networks (CNNs) achieve remarkable recognition performance. However, there is still lack of understanding about the generalization mechanism of action recognition models. In this paper, we suggest that action recognition models rely on the motion information less than expected, and thus they are robust to randomization of frame orders. Furthermore, we find that motion monotonicity remaining after randomization also contributes to such robustness. Based on this observation, we develop a novel defense method using temporal shuffling of input videos against adversarial attacks for action recognition models. Another observation enabling our defense method is that adversarial perturbations on videos are sensitive to temporal destruction. To the best of our knowledge, this is the first attempt to design a defense method without additional training for 3D CNN-based video action recognition models. Adversarial attack/defense Action recognition Temporal information in action recognition Zhang, Huan verfasserin aut Choi, Jun-Ho verfasserin aut Hsieh, Cho-Jui verfasserin aut Lee, Jong-Seok verfasserin (orcid)0000-0002-8038-1119 aut Enthalten in Neural networks Amsterdam : Elsevier, 1988 169, Seite 388-397 Online-Ressource (DE-627)302468536 (DE-600)1491372-0 (DE-576)07971997X 1879-2782 nnns volume:169 pages:388-397 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 54.72 Künstliche Intelligenz VZ AR 169 388-397
allfields_unstemmed	10.1016/j.neunet.2023.10.033 doi (DE-627)ELV066194377 (ELSEVIER)S0893-6080(23)00588-9 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Hwang, Jaehui verfasserin (orcid)0009-0003-7785-6886 aut Temporal shuffling for defending deep action recognition models against adversarial attacks 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, video-based action recognition methods using convolutional neural networks (CNNs) achieve remarkable recognition performance. However, there is still lack of understanding about the generalization mechanism of action recognition models. In this paper, we suggest that action recognition models rely on the motion information less than expected, and thus they are robust to randomization of frame orders. Furthermore, we find that motion monotonicity remaining after randomization also contributes to such robustness. Based on this observation, we develop a novel defense method using temporal shuffling of input videos against adversarial attacks for action recognition models. Another observation enabling our defense method is that adversarial perturbations on videos are sensitive to temporal destruction. To the best of our knowledge, this is the first attempt to design a defense method without additional training for 3D CNN-based video action recognition models. Adversarial attack/defense Action recognition Temporal information in action recognition Zhang, Huan verfasserin aut Choi, Jun-Ho verfasserin aut Hsieh, Cho-Jui verfasserin aut Lee, Jong-Seok verfasserin (orcid)0000-0002-8038-1119 aut Enthalten in Neural networks Amsterdam : Elsevier, 1988 169, Seite 388-397 Online-Ressource (DE-627)302468536 (DE-600)1491372-0 (DE-576)07971997X 1879-2782 nnns volume:169 pages:388-397 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 54.72 Künstliche Intelligenz VZ AR 169 388-397
allfieldsGer	10.1016/j.neunet.2023.10.033 doi (DE-627)ELV066194377 (ELSEVIER)S0893-6080(23)00588-9 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Hwang, Jaehui verfasserin (orcid)0009-0003-7785-6886 aut Temporal shuffling for defending deep action recognition models against adversarial attacks 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, video-based action recognition methods using convolutional neural networks (CNNs) achieve remarkable recognition performance. However, there is still lack of understanding about the generalization mechanism of action recognition models. In this paper, we suggest that action recognition models rely on the motion information less than expected, and thus they are robust to randomization of frame orders. Furthermore, we find that motion monotonicity remaining after randomization also contributes to such robustness. Based on this observation, we develop a novel defense method using temporal shuffling of input videos against adversarial attacks for action recognition models. Another observation enabling our defense method is that adversarial perturbations on videos are sensitive to temporal destruction. To the best of our knowledge, this is the first attempt to design a defense method without additional training for 3D CNN-based video action recognition models. Adversarial attack/defense Action recognition Temporal information in action recognition Zhang, Huan verfasserin aut Choi, Jun-Ho verfasserin aut Hsieh, Cho-Jui verfasserin aut Lee, Jong-Seok verfasserin (orcid)0000-0002-8038-1119 aut Enthalten in Neural networks Amsterdam : Elsevier, 1988 169, Seite 388-397 Online-Ressource (DE-627)302468536 (DE-600)1491372-0 (DE-576)07971997X 1879-2782 nnns volume:169 pages:388-397 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 54.72 Künstliche Intelligenz VZ AR 169 388-397
allfieldsSound	10.1016/j.neunet.2023.10.033 doi (DE-627)ELV066194377 (ELSEVIER)S0893-6080(23)00588-9 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Hwang, Jaehui verfasserin (orcid)0009-0003-7785-6886 aut Temporal shuffling for defending deep action recognition models against adversarial attacks 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recently, video-based action recognition methods using convolutional neural networks (CNNs) achieve remarkable recognition performance. However, there is still lack of understanding about the generalization mechanism of action recognition models. In this paper, we suggest that action recognition models rely on the motion information less than expected, and thus they are robust to randomization of frame orders. Furthermore, we find that motion monotonicity remaining after randomization also contributes to such robustness. Based on this observation, we develop a novel defense method using temporal shuffling of input videos against adversarial attacks for action recognition models. Another observation enabling our defense method is that adversarial perturbations on videos are sensitive to temporal destruction. To the best of our knowledge, this is the first attempt to design a defense method without additional training for 3D CNN-based video action recognition models. Adversarial attack/defense Action recognition Temporal information in action recognition Zhang, Huan verfasserin aut Choi, Jun-Ho verfasserin aut Hsieh, Cho-Jui verfasserin aut Lee, Jong-Seok verfasserin (orcid)0000-0002-8038-1119 aut Enthalten in Neural networks Amsterdam : Elsevier, 1988 169, Seite 388-397 Online-Ressource (DE-627)302468536 (DE-600)1491372-0 (DE-576)07971997X 1879-2782 nnns volume:169 pages:388-397 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 54.72 Künstliche Intelligenz VZ AR 169 388-397
language	English
source	Enthalten in Neural networks 169, Seite 388-397 volume:169 pages:388-397
sourceStr	Enthalten in Neural networks 169, Seite 388-397 volume:169 pages:388-397
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Adversarial attack/defense Action recognition Temporal information in action recognition
dewey-raw	004
isfreeaccess_bool	false
container_title	Neural networks
authorswithroles_txt_mv	Hwang, Jaehui @@aut@@ Zhang, Huan @@aut@@ Choi, Jun-Ho @@aut@@ Hsieh, Cho-Jui @@aut@@ Lee, Jong-Seok @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	302468536
dewey-sort	14
id	ELV066194377
language_de	englisch
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author	Hwang, Jaehui
spellingShingle	Hwang, Jaehui ddc 004 bkl 54.72 misc Adversarial attack/defense misc Action recognition misc Temporal information in action recognition Temporal shuffling for defending deep action recognition models against adversarial attacks
authorStr	Hwang, Jaehui
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topic_title	004 VZ 54.72 bkl Temporal shuffling for defending deep action recognition models against adversarial attacks Adversarial attack/defense Action recognition Temporal information in action recognition
topic	ddc 004 bkl 54.72 misc Adversarial attack/defense misc Action recognition misc Temporal information in action recognition
topic_unstemmed	ddc 004 bkl 54.72 misc Adversarial attack/defense misc Action recognition misc Temporal information in action recognition
topic_browse	ddc 004 bkl 54.72 misc Adversarial attack/defense misc Action recognition misc Temporal information in action recognition
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title	Temporal shuffling for defending deep action recognition models against adversarial attacks
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title_full	Temporal shuffling for defending deep action recognition models against adversarial attacks
author_sort	Hwang, Jaehui
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author_browse	Hwang, Jaehui Zhang, Huan Choi, Jun-Ho Hsieh, Cho-Jui Lee, Jong-Seok
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title_sort	temporal shuffling for defending deep action recognition models against adversarial attacks
title_auth	Temporal shuffling for defending deep action recognition models against adversarial attacks
abstract	Recently, video-based action recognition methods using convolutional neural networks (CNNs) achieve remarkable recognition performance. However, there is still lack of understanding about the generalization mechanism of action recognition models. In this paper, we suggest that action recognition models rely on the motion information less than expected, and thus they are robust to randomization of frame orders. Furthermore, we find that motion monotonicity remaining after randomization also contributes to such robustness. Based on this observation, we develop a novel defense method using temporal shuffling of input videos against adversarial attacks for action recognition models. Another observation enabling our defense method is that adversarial perturbations on videos are sensitive to temporal destruction. To the best of our knowledge, this is the first attempt to design a defense method without additional training for 3D CNN-based video action recognition models.
abstractGer	Recently, video-based action recognition methods using convolutional neural networks (CNNs) achieve remarkable recognition performance. However, there is still lack of understanding about the generalization mechanism of action recognition models. In this paper, we suggest that action recognition models rely on the motion information less than expected, and thus they are robust to randomization of frame orders. Furthermore, we find that motion monotonicity remaining after randomization also contributes to such robustness. Based on this observation, we develop a novel defense method using temporal shuffling of input videos against adversarial attacks for action recognition models. Another observation enabling our defense method is that adversarial perturbations on videos are sensitive to temporal destruction. To the best of our knowledge, this is the first attempt to design a defense method without additional training for 3D CNN-based video action recognition models.
abstract_unstemmed	Recently, video-based action recognition methods using convolutional neural networks (CNNs) achieve remarkable recognition performance. However, there is still lack of understanding about the generalization mechanism of action recognition models. In this paper, we suggest that action recognition models rely on the motion information less than expected, and thus they are robust to randomization of frame orders. Furthermore, we find that motion monotonicity remaining after randomization also contributes to such robustness. Based on this observation, we develop a novel defense method using temporal shuffling of input videos against adversarial attacks for action recognition models. Another observation enabling our defense method is that adversarial perturbations on videos are sensitive to temporal destruction. To the best of our knowledge, this is the first attempt to design a defense method without additional training for 3D CNN-based video action recognition models.
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title_short	Temporal shuffling for defending deep action recognition models against adversarial attacks
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author2	Zhang, Huan Choi, Jun-Ho Hsieh, Cho-Jui Lee, Jong-Seok
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doi_str	10.1016/j.neunet.2023.10.033
up_date	2024-07-06T16:54:42.048Z
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Temporal shuffling for defending deep action recognition models against adversarial attacks

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