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Work | Devices | Sensors | Scenario | Features | Algorithms | Results |
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[18] | 20 Androids | Gyroscope, accelerometer, magnetometer, microphone, and vibrator | 4 scenarios: (a) smartphone on a table with and without vibration and (a) smartphone held in the hand with and without vibration | Time-domain and frequency-domain features | Random forest and naive Bayes | Accelerometer accuracy higher than both sensors. With the combination of all sensors, the identification accuracy exceeds 90% |
[191] | 17 Androids and 17 IOS | Microphone, speakers, and accelerometer | Three scenarios (wooden desk, metal cabinet, and windowsill) | Frequency response and FFT value | Maximum likelihood estimation (MLE), simple Euclidean distance-based classification, and -NN classification | 95% accuracy with a microphone and speaker and more than 98% for both |
[180] | 10 Androids | Accelerometer, gyroscope, magnetometer, microphone, camera, and vibrator | Flat wooden surface and hand-held | Photo response nonuniformity (PRNU), time-domain and frequency-domain features | Bagged decision tree | High accuracy for gyroscope and accelerometer, 100% for the combination of both |
[192] | 4 IOS, 1 Blackberry, and 8 Androids | Camera | — | Wavelet features, photo response nonuniformity (PRNU) | SVM | Accuracy of approximately 94% |
[193] | 8000 IOS | All sensors and context features | — | 29 different features | SVM and random classifier | Accuracy of approximately 97% |
[194] | 6 cameras and 3 smartphones | Camera | — | Color, quality, and frequency-domain features | SVM | Accuracy between 66% and 97% |
[195] | 12 smartphones and camera | Camera | — | Color, quality, frequency domain, and wavelet feature+PRUN | SVM | For all features, accuracy increases. Some features obtain better results in specific scenarios |
[196] | Arduino and accelerometer | Accelerometer | On a flat table | Time-domain features | Statistical | Each accelerometer chip has its own fingerprint |
[25] | 3 smartphones from three vendors | Accelerometer and gyroscope | On a flat table | Time-domain features | SVM | Accuracy more than 90% |
[181] | 30 between IOS and Android | Accelerometer and gyroscope | On a table | Time- and frequency-domain features | SVM, naive Bayes, multiclass decision tree, -nearest neighbor (KNN), quadratic discriminant Analysis (QDA) classifier and Bagged Decision Trees | Bagged decision trees have the highest accuracy |
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