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Table 7 Transferability of adversarial samples when both the source model (with Mixed LSTM architecture) and target models (with other architectures) are BEAM-related models. The N of GPBEAM-DE is 5. Since the adversarial samples are all obtained from the source model, the values of \({DL}_{B}\) or \({DL}_{E}\) are the same for all architectures, and so only the \({DL}_{B}\) and \({DL}_{E}\) of Mixed LSTM (source) are shown here

From: Perturbing BEAMs: EEG adversarial attack to deep learning models for epilepsy diagnosing

Architecture

Maxpool

Temporal convolution

LSTM

Mixed LSTM (source)

\(\epsilon\)

Evaluation Criteria

Acc

SR

Acc

SR

Acc

SR

Acc

SR

\({\mathrm{DL}}_{B}\)

\({\mathrm{DL}}_{E}\)

-

-

0.92

-

0.92

-

0.93

-

0.94

-

-

-

0.1

\(\mathrm{GPBEAM}\)

0.80

0.15

0.79

0.14

0.80

0.15

0.73

0.20

0.11

0.024

GPBEAM-DE

0.83

0.13

0.76

0.18

075

0.19

0.64

0.31

0.06

0.021

0.3

\(\mathrm{GPBEAM}\)

0.67

0.27

0.66

0.28

0.65

0.29

0.61

0.32

0.32

0.030

GPBEAM-DE

0.46

0.48

0.39

0.52

0.44

0.49

0.25

0.70

0.18

0.026

0.5

\(\mathrm{GPBEAM}\)

0.62

0.32

0.60

0.33

0.61

0.32

0.56

0.37

0.53

0.036

GPBEAM-DE

0.36

0.60

0.32

0.66

0.35

0.62

0.15

0.80

0.29

0.030