多视图嵌入与感知显著性图像哈希在篡改检测中的应用
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"这篇研究论文探讨了一种新的图像哈希方法，用于图像篡改检测，该方法结合了多视图嵌入和感知显著性。通过这种方法，可以更有效地识别和定位图像中的篡改部分，提高了篡改检测的准确性和效率。"
在图像篡改检测领域，基于感知哈希的技术因其快速和内存效率而备受关注。感知哈希能够通过比较原始图像与疑似篡改图像的哈希值差异来检测图像是否被修改。然而，现有的大多数方法在生成哈希码时，忽略了图像中不同区域的感知显著性差异，即哪些区域对人类视觉系统来说更为重要。
本文提出了一种创新的方法，它引入了多视图嵌入的概念，这允许从不同的角度和特征层次来表示图像。多视图嵌入可以捕获图像的复杂结构和多样性，从而提供更全面的图像表示。此外，结合感知显著性，该方法能够强调图像中对人类视觉系统最显眼的部分，这些部分通常是篡改的潜在目标。通过这种方式，算法可以更加精确地定位篡改活动，并且对微小篡改也具有较高的敏感度。
在实践中，首先对图像进行分割，然后计算每个区域的感知显著性得分。这些得分用于指导哈希码的生成过程，确保关键区域的哈希值具有更高的区分度。通过深度学习模型，可以训练一个监督系统，该系统在大量有标签的数据集上学习如何生成高质量的哈希码，同时考虑了每个区域的重要性。
文章进一步讨论了实验设计和结果分析，验证了所提方法在篡改检测任务上的优越性能。与其他传统方法相比，该方法在多种基准数据集上的实验结果显示出了更高的检测准确率和鲁棒性。此外，由于其利用了感知显著性，该方法在处理局部篡改和复杂篡改场景时表现尤为突出。
这篇研究论文提出了一种新颖的图像哈希策略，将多视图嵌入与感知显著性相结合，以提高图像篡改检测的效果。这种方法对于提升数字媒体的安全性，特别是在法律证据、新闻报道和社交媒体等领域，具有重要的应用价值。同时，这种方法也为未来的研究提供了新的思路，即在哈希编码中考虑图像的感知特性，以便更好地服务于计算机视觉和图像处理的相关任务。
Advances in Multimedia
= 116
𝑤
−16,
()
= 500
𝑤
−
𝑤
,
()
= 200
𝑤
−
𝑤
,
()
where R, G, and B are the red, green, and blue component of a
pixel, X, Y, and Z are the CIE XYZ tristimulus values (), and
L, A, and B ((), (), and ()) are color lightness, chromaticity,
and coordinates, respectively. Xw=., Yw=., and
Zw=. are the CIE XYZ tristimulus values of the
reference white point, and f(t) is calculated by the following
rule:
(
)
=
1/3
, > 0.008856
7.787+
16
116
,,
()
and the L component is then taken for image representa-
tion (Figure (c)). Integer Wavelet Transform (IntWT) is an
approximation of original image and is more robust against
signal processing attacks. erefore, we nally apply one-
level IntWT to the L component and take the low frequency
subband (LL) as the semantic perceptual image (Figure (d)),
from which multiple types of feature are extracted for hash
generation.
2.2. Hashing Learning. Suppose there are images in the
given whole set, represented as ={x
𝑖
}, =1,2,...,,where
x
𝑖
∈ R
𝐷
represents feature vector. For each image, we extract
their types of features. e task of multiview perceptual
image hashing is to learn hash functions by simultaneously
utilizing the feature matrices X
(1)
,X
(2)
,...,X
(𝑉)
,withX
(V)
=
[x
(V)
1
,x
(V)
2
,...,x
(V)
𝑛
]corresponding to the V-type of feature
matrix. Let X ={X
1
: X
2
:⋅⋅⋅:X
𝑛
} denote the combined
matrix for multiview feature, where X ∈R
𝐷×𝑛
, =∑
𝑉
V=1
V
,
and
V
is the dimension of V- type feature. e goal of
our algorithm is to learn hash functions that map X ∈
R
𝐷×𝑛
to a compact representation B
𝐾×𝑛
in a low-dimensional
Hamming space, where is the digits length.
In the set ,thereare labeled images, ,which
are associated with at least one of the two categorizes M
and C.Specically,apair(x
𝑖
,x
𝑗
)∈M is denoted as
perceptually similar pair when (x
𝑖
,x
𝑗
) are the images that
have been under content-preserved un-malicious distortions
and attacks. (x
𝑖
,x
𝑗
)∈C is denoted as perceptually dissimilar
pair when two samples are the original image and the one
that is suered from malicious manipulations or perceptually
signicant attacks such as object insertion and removal. Let
us denote the feature matrix formed by the corresponding
columns of X as X
𝑙
∈ R
𝐷×𝑙
. Note that the feature matrices are
normalized to zero-centered.
We dene the perceptual condence measurement for
each image example. e matrix S ∈ R
𝑙×𝑙
incorporating the
pairwise labeled information from X
𝑙
,
𝑖𝑗
is the pairwise
relationship for (x
𝑖
,x
𝑗
), which is dened as
S
𝑖𝑗
=
1x
𝑖
,x
𝑗
∈M
−1 x
𝑖
,x
𝑗
∈C
0.
()
Supposewewanttolearnhash functions that leading to
a -digit representation B of X.Foreachdigit=1,2,...,,
its hash function is dened as
𝑘
x
𝑖
=w
T
𝑘
x
𝑖
,
()
where w
𝑘
∈ R
𝐷
is the coecient vector. Let W =[w
1
,w
2
,
...,w
𝑘
]∈R
𝐷×𝐾
and the representation B of the feature
matrix X for image set is
B = W
T
X.
()
Our goal is to learn a W that is simultaneously maximizing
the empirical accuracy on the labeled image and variance
of hash bits over all images. e empirical accuracy on the
labeledimageisdenedas
1
(
W
)
=
𝑘
(x
𝑖
,x
𝑗
)∈M
S
𝑖𝑗
𝑘
x
𝑖
𝑘
x
𝑗
+
(x
𝑖
,x
𝑗
)∈C
S
𝑖𝑗
𝑘
x
𝑖
𝑘
x
𝑗
.
()
e objective function for empirical accuracy can be repre-
sented as
1
(
W
)
=
1
2
tr W
T
X
𝑙
S W
T
X
𝑙
T
.
()
en, the empirical accuracy
1
(W)is presented as
1
(
W
)
=
1
2
tr
W
T
X
𝑙
SX
T
𝑙
W
.
()
Moreover, to maximize the information provided by each bit,
the variance of hash bits over all data X is also measured and
taken as a regularization term:
(
W
)
=
𝑘
var
𝑘
(
X
)
=
𝑘
var w
T
𝑘
X.
()
Maximizing the above function with respect to W is still hard
due to its nondierentiability. As the maximum variance of
a hash function is lower bounded by the scaled variance of
the projected data, the information theoretic regularization
is represented as
2
(
W
)
=
1
2
tr W
T
XW
T
X
T
.
()
Finally, the overall semi-supervised objective function
combines the relaxed empirical tness term from () and
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