FEBRUARY 2020 | VOL. 63 | NO. 2 | COMMUNICATIONS OF THE ACM 87
attacker suffers intolerable image corruption (PSNR = 13dB,
CW-SSIM = 0.56, CIEDE2000 = 34) by combining same number of randomly selected frames (“Illumination intensity
randomization” section).
For the dynamic scene, we set fintra = 1 kHz and finter = 300 Hz
(Section 3. 1). From Figure 9, we can see the authorized user
has much higher quality (PSNR = 30dB, CW-SSIM = 0.98 in
average) compared with attacker (PSNR = 10dB, CW-SSIM = 0.6
in average). This can be seen by resulting image frames in
Figure 8, where attacker suffers from both intra-frame and
inter-frame stripes. Thus LiShield’s authorization scheme is
effective in unblocking specific users while maintaining protection against attackers.
6. 3. Effectiveness of barcode embedding
We first determine general optimal parameters for LiShield’s
barcode detector in Section 4, based on the following metrics. (i) False alarm rate. We run the detector on 200 images
(random real-world scenes) and measure the probability
that a barcode is detected from clean image. (ii) Detection
rate. We embed monochrome barcodes with different f1
from 400 Hz to 10 kHz with 200 Hz switching frequency.
For each f1, we embed three frequencies with ∆f = 200 Hz
interval and capture 300 images with these barcodes over
a benchmark scene to obtain detection rate. Considering
the trade-off between false alarm and detection, we choose
Tb = 0.05 to bound the false alarm rate below 5%, while
ensuring around 90% detection rate for monochrome barcode (Figure 10).
Using the above configuration, we found the detec-
tion rate for RGB barcode is close to 100% with or with-
out manual exposure attack, while being slightly below
Two bonus effects from our RGB LED are observed: (i)
The structural distortion from the stripes disrupts the cam-
era’s auto-focus function, often making the captured scene
extremely blur. This is because under LiShield, contrast
of bands no longer depends on focusing accuracy, which
breaks the assumption of auto-focus mechanism. (ii) The
color bands also mislead the automatic white balance function
across all five different scenes, since the camera can no lon-
ger identify a clean region in the image to calibrate itself and
thus hesitates.
Impact on dynamic scenes. To create a dynamic scene,
we use the motor to rotate the smartphone, creating relative
motion at three different speeds ( 45, 100, and 145 degrees/
second). Our experiment shows the average CW-SSIM
among all three speeds further decreases by 0.1, which
indicates that dynamic scene experiences worse quality under
LiShield due to motion blur. Moreover, if the exposure time
is larger than 1/100 s, then overexposure and motion blurs
together further reduce the quality (PSNR < 6, CW-SSIM
< 0.1). Thus, dynamic objects further decrease the adjustment
range of exposure time and make manual exposure attack
more ineffective.
6. 2. Effectiveness of user authorization
We developed an app (Section 5) that allows a user to capture critical frames on static scene protected by our RGB
LED and then recover the scene following Section 3. The
resulting image quality (PSNR = 25dB, CW-SSIM = 0.9,
CIEDE2000 = 5) is comparable to the ideal setting when we
disable LiShield’s LED modulation (Figure 8 shows example
frames extracted from a recorded video). In contrast, the
Unprotected Authorized Attacker
Figure 8. Frames observed by authorized users and attackers.
0
0.2
0.4
0.6
0.8
1
0 30 60 90 120 150 180
C
W
-S
S
IM
Frame Number
Auth. Att.
Figure 9. Video quality with and without authorization.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Mono Mono+Att RGB RGB+Att
D
et
e
c
ti
on
Ra
te
Figure 10. Detection rate of monochrome and RGB barcode design.
Freq = 100 Hz
PSNR = 8
CWSSIM = 0.27
CIEDE2000 = 22
Freq = 200 Hz
PSNR = 9
CWSSIM = 0.35
CIEDE2000 = 20
Freq = 300 Hz
PSNR = 11
CWSSIM = 0.39
CIEDE2000 = 18
Freq = 400 Hz
PSNR = 12
CWSSIM = 0.43
CIEDE2000 = 15
Freq = 500 Hz
PSNR = 16
CWSSIM = 0.44
CIEDE2000 = 12
Freq = 100 Hz
PSNR = 16
CWSSIM = 0.39
CIEDE2000 = 37
Freq = 200 Hz
PSNR = 13
CWSSIM = 0.36
CIEDE2000 = 33
Freq = 300 Hz
PSNR = 15
CWSSIM = 0.4
CIEDE2000 = 28
Freq = 400 Hz
PSNR = 16
CWSSIM = 0.41
CIEDE2000 = 21
Freq = 500 Hz
PSNR = 17
CWSSIM = 0.42
CIEDE2000 = 15
Figure 6. Image quality levels on a benchmark image.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
100Hz 200Hz 300Hz 400Hz 500Hz
CWS
SIM
Flickering Frequency
1/1000 s
1/500 s
1/200 s
1/100 s
1/50 s
Figure 7. Quality with fix-exposure camera.