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MLAttack: Fooling Semantic Segmentation Networks by Multi-layer Attacks

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoPattern Recognition - 41st DAGM German Conference, DAGM GCPR 2019, Proceedings
ToimittajatGernot A. Fink, Simone Frintrop, Xiaoyi Jiang
KustantajaSpringer
Sivut401-413
Sivumäärä13
ISBN (painettu)9783030336752
DOI - pysyväislinkit
TilaJulkaistu - 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaDAGM German Conference on Pattern Recognition - Dortmund, Saksa
Kesto: 10 syyskuuta 201913 syyskuuta 2019

Julkaisusarja

NimiLecture Notes in Computer Science
Vuosikerta11824 LNCS
ISSN (painettu)0302-9743
ISSN (elektroninen)1611-3349

Conference

ConferenceDAGM German Conference on Pattern Recognition
MaaSaksa
KaupunkiDortmund
Ajanjakso10/09/1913/09/19

Tiivistelmä

Despite the immense success of deep neural networks, their applicability is limited because they can be fooled by adversarial examples, which are generated by adding visually imperceptible and structured perturbations to the original image. Semantic segmentation is required in several visual recognition tasks, but unlike image classification, only a few studies are available for attacking semantic segmentation networks. The existing semantic segmentation adversarial attacks employ different gradient based loss functions which are defined using only the last layer of the network for gradient backpropogation. But some components of semantic segmentation networks implicitly mitigate several adversarial attacks (like multiscale analysis) due to which the existing attacks perform poorly. This provides us the motivation to introduce a new attack in this paper known as MLAttack, i.e., Multiple Layers Attack. It carefully selects several layers and use them to define a loss function for gradient based adversarial attack on semantic segmentation architectures. Experiments conducted on publicly available dataset using the state-of-the-art segmentation network architectures, demonstrate that MLAttack performs better than existing state-of-the-art semantic segmentation attacks.

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