3. 2. Static scene recovery
When the target scene is static, the authorized user may
capture a few complementary frames at a specific time to
recover the scene as depicted in Figure 3, where frequency
and intensity randomization (Section 2. 2) are employed in
each frame to ensure robustness. Although it does require
recording a very short video, the process is extremely short
(200 ms at most) and barely noticeable to the authorized
user. Meanwhile, an out-of-sync attacker will still receive
corrupted images that cannot reconstruct the original scene
by direct frame combination.
Suppose a static scene is to be recovered using Lf frames,
referred to as critical frames. To prevent attackers from
launching the multiframe attack, the timing of the critical
frames is negotiated only between the smart LED and the
authorized user through the secure side channel. These Lf
frames together must contain the information of the entire
scene, that is, they must be complementary, as shown in
Figure 3. Meanwhile, all other frames will follow the normal
flickering pattern. Since the attackers can neither identify
nor predict the timing of the critical frames, the best they
can do is to launch the brute-force multiframe attack, which
has proven to be ineffective (“Illumination intensity randomization” section).
4. AUTOMATIC PHYSICAL WATERMARKING FOR
PRIVACY ENFORCEMENT
High-intensity ambient light sources (e.g., sunlight, legacy lighting, and ash lights) can create strong interference
to LiShield’s illumination waveform, degrading the contrast by adding a constant intensity to both the bright and
dark stripes, which may weaken LiShield’s protection. In
such scenarios, LiShield degrades itself to a barcode mode,
where it embeds barcode in the physical scene to convey
privacy policies. The barcode forms low-contrast stripes,
which may not fully corrupt the images of the scene, but
can still be detected by online photo-distributing hubs
(e.g., social website servers), which automatically enforce
the policies, without co-operation of the uploader or
evidence visible by naked eye. LiShield forms the watermark with just a single light fixture, instead of active displays (e.g., projectors) that are required by conventional
systems. The key challenge here is how should LiShield
encode the information, so that it can be robustly conveyed to the policy enforcers, despite the (uncontrollable)
attacker camera settings?
Embedding. LiShield’s barcode packs multiple frequen-
cies in every image following “Frequency scrambling” sec-
tion but aims to map the ratios between frequencies into
digital information. Suppose LiShield embeds two wave-
forms with frequencies F0 and F1, it chooses the two frequency
components such that F1/F0 equals to a value Rp well known
to the policy enforcers. In other words, the presence of Rp
conveys “no distribution/sharing allowed.” Although width
of stripes is affected by sampling interval ts and exposure
time te (Figure 1(a) and (b)), ratio of stripe widths resulted
from two frequencies (which equals to Rp) remains constant.
Therefore, this encoding mechanism is robust against cam-
era settings.
Since physical scenes usually comprise a mix of spatial
frequencies, and spectral power rolls off in higher spatial
frequencies, thanks to camera lenses’ limited bandwidth
while temporal frequencies are unaffected, LiShield’s barcode uses frequencies that are much higher than the natural
frequencies (>400 Hz) in the scene to reduce interference.
It is worth noting that since the rolling-shutter sampling
rate of all cameras falls in a range ( 30 kHz to slightly over
100 kHz20), LiShield limits its highest flickering frequency
to 15 kHz, which respects the Nyquist sampling theorem,
so that the barcode can eventually be recovered without any
aliasing effect.
To further improve robustness, LiShield leverages redundancy. It embeds multiple pairs of frequency components
to make multiple values of Rp either at different rows of the
image or in different color channels, further mitigating
interference caused by intrinsic spatial patterns within the
scene.
Detection. Since the barcode contains M frequencies,
that is, fn = fB + (n − 1)∆f, n = 2, 3, …, M (“Frequency scrambling” section), there are MR = C2 M possible frequency
ratio values across the image for monochrome barcode
(MR = C2 M× 3 for RGB barcode). ∆f must be set large enough
to avoid confusion (∆f = 200 Hz in experiments). The barcode decoder, running on the policy enforcer, recognizes
the image as protected if there are at least Mb values
that roughly match the known ratio Rp, that is, when the
value falls within Tb of Rp. Suppose Matt is the number of Rp
removed by manual exposure attack (Section 2. 2), these
parameters are determined by bounding the false positive
rate following an empirical procedure (to be discussed in
Section 6. 3).
To detect the frequency ratios, LiShield averages the
intensity of each row to get a one-dimension time series sr.
LiShield then runs FFT over each series to extract the Mp
strongest frequencies. Finally, LiShield combines all unique
frequencies extracted and computes all frequency ratios.
The redundancy in barcode ensures that it can be robustly
detected.
5. IMPLEMENTATION
Testbed setup. Figure 4 shows our smart LED prototype, and
the target scenes containing five capture-sensitive objects
(document and painting are 2-D objects and others are all
3-D objects). We mount the LED inside a diffusive plastic
cover similar to conventional ceiling light covers. We use a
programmable motor to hold the camera and control its dis-tance/orientation, in order to create static or dynamic scene
setup in a repeatable manner.
Frame 2 Frame 1
+=
Authorized User Attacker
Figure 3. The impact of multiframe recovery on authorized user and
attacker, respectively.