Citation: Beijing Chen, Ye Gao, Lingzheng Xu, Xiaopeng Hong, Yuhui Zheng, Yun-Qing Shi. Color image splicing localization algorithm by quaternion fully convolutional networks and superpixel-enhanced pairwise conditional random field[J]. Mathematical Biosciences and Engineering, 2019, 16(6): 6907-6922. doi: 10.3934/mbe.2019346
[1] | N. E. Cho, G. Murugusundaramoorthy, K. R. Karthikeyan, S. Sivasubramanian . Properties of λ-pseudo-starlike functions with respect to a boundary point. AIMS Mathematics, 2022, 7(5): 8701-8714. doi: 10.3934/math.2022486 |
[2] | Pinhong Long, Huo Tang, Wenshuai Wang . Functional inequalities for several classes of q-starlike and q-convex type analytic and multivalent functions using a generalized Bernardi integral operator. AIMS Mathematics, 2021, 6(2): 1191-1208. doi: 10.3934/math.2021073 |
[3] | Sadaf Umar, Muhammad Arif, Mohsan Raza, See Keong Lee . On a subclass related to Bazilevič functions. AIMS Mathematics, 2020, 5(3): 2040-2056. doi: 10.3934/math.2020135 |
[4] | Mohammad Faisal Khan, Jongsuk Ro, Muhammad Ghaffar Khan . Sharp estimate for starlikeness related to a tangent domain. AIMS Mathematics, 2024, 9(8): 20721-20741. doi: 10.3934/math.20241007 |
[5] | Wenzheng Hu, Jian Deng . Hankel determinants, Fekete-Szegö inequality, and estimates of initial coefficients for certain subclasses of analytic functions. AIMS Mathematics, 2024, 9(3): 6445-6467. doi: 10.3934/math.2024314 |
[6] | Hava Arıkan, Halit Orhan, Murat Çağlar . Fekete-Szegö inequality for a subclass of analytic functions defined by Komatu integral operator. AIMS Mathematics, 2020, 5(3): 1745-1756. doi: 10.3934/math.2020118 |
[7] | Pinhong Long, Xing Li, Gangadharan Murugusundaramoorthy, Wenshuai Wang . The Fekete-Szegö type inequalities for certain subclasses analytic functions associated with petal shaped region. AIMS Mathematics, 2021, 6(6): 6087-6106. doi: 10.3934/math.2021357 |
[8] | K. R. Karthikeyan, G. Murugusundaramoorthy, N. E. Cho . Some inequalities on Bazilevič class of functions involving quasi-subordination. AIMS Mathematics, 2021, 6(7): 7111-7124. doi: 10.3934/math.2021417 |
[9] | Muhammad Ghaffar Khan, Sheza.M. El-Deeb, Daniel Breaz, Wali Khan Mashwani, Bakhtiar Ahmad . Sufficiency criteria for a class of convex functions connected with tangent function. AIMS Mathematics, 2024, 9(7): 18608-18624. doi: 10.3934/math.2024906 |
[10] | Ahmad A. Abubaker, Khaled Matarneh, Mohammad Faisal Khan, Suha B. Al-Shaikh, Mustafa Kamal . Study of quantum calculus for a new subclass of q-starlike bi-univalent functions connected with vertical strip domain. AIMS Mathematics, 2024, 9(5): 11789-11804. doi: 10.3934/math.2024577 |
Let A denote the class of functions of the form
f(z)=z+a2z2+a3z3+a4z4+⋯, | (1.1) |
which are analytic in the open unit disk D=(z:∣z∣<1) and normalized by f(0)=0 and f′(0)=1. Recall that, S⊂A is the univalent function in D=(z:∣z∣<1) and has the star-like and convex functions as its sub-classes which their geometric condition satisfies Re(zf′(z)f(z))>0 and Re(1+zf′′(z)f′(z))>0. The two well-known sub-classes have been used to define different subclass of analytical functions in different direction with different perspective and their results are too voluminous in literature.
Two functions f and g are said to be subordinate to each other, written as f≺g, if there exists a Schwartz function w(z) such that
f(z)=g(w(z)),zϵD | (1.2) |
where w(0) and ∣w(z)∣<1 for zϵD. Let P denote the class of analytic functions such that p(0)=1 and p(z)≺1+z1−z, zϵD. See [1] for details.
Goodman [2] proposed the concept of conic domain to generalize convex function which generated the first parabolic region as an image domain of analytic function. The same author studied and introduced the class of uniformly convex functions which satisfy
UCV=Re{1+(z−ψ)f″(z)f′(z)}>0,(z,ψ∈A). |
In recent time, Ma and Minda [3] studied the underneath characterization
UCV=Re{1+zf″(z)f′(z)>|zf″(z)f′(z)|},zϵD. | (1.3) |
The characterization studied by [3] gave birth to first parabolic region of the form
Ω={w;Re(w)>∣w−1∣}, | (1.4) |
which was later generalized by Kanas and Wisniowska ([5,6]) to
Ωk={w;Re(w)>k∣w−1∣,k≥0}. | (1.5) |
The Ωk represents the right half plane for k=0, hyperbolic region for 0<k<1, parabolic region for k=1 and elliptic region for k>1 [30].
The generalized conic region (1.5) has been studied by many researchers and their interesting results litter everywhere. Just to mention but a few Malik [7] and Malik et al. [8].
More so, the conic domain Ω was generalized to domain Ω[A,B], −1≤B<A≤1 by Noor and Malik [9] to
Ω[A,B]={u+iv:[(B2−1)(U2+V2)−2(AB−1)u+(A2−1)]2 |
>[−2(B+1)(u2v2)+2(A+B+C)u−2(A+1)]2+4(A−B)2v2} |
and it is called petal type region.
A function p(z) is said to be in the class UP[A,B], if and only if
p(z)≺(A+1)˜p(z)−(A−1)(B+1)˜p(z)−(B−1), | (1.6) |
where ˜p(z)=1+2π2(log1+√z1−√z)2.
Taking A=1 and B=−1 in (1.8), the usual classes of functions studied by Goodman [1] and Kanas ([5,6]) will be obtained.
Furthermore, the classes UCV[A,B] and ST[A,B] are uniformly Janoski convex and Starlike functions satisfies
Re((B−1)(zf′(z))′f′(z)−(A−1)(B+1)(zf′(z))′f′(z)−(A+1))>|(B−1)(zf′(z))′f′(z)−(A−1)(B+1)(zf′(z))′f′(z)−(A+1)−1| | (1.7) |
and
Re((B−1)zf′(z)f′(z)−(A−1)(B+1)zf′(z)f′(z)−(A+1))>|(B−1)zf′(z)f′(z)−(A−1)(B+1)zf′(z)f′(z)−(A+1)−1|, | (1.8) |
or equivalently
(zf′(z))′f′(z)∈UP[A,B] |
and
zf′(z)f′(z)∈UP[A,B]. |
Setting A=1 and B=−1 in (1.7) and (1.8), we obtained the classes of functions investigated by Goodman [2] and Ronning [10].
The relevant connection to Fekete-Szegö problem is a way of maximizing the non-linear functional |a3−λa22| for various subclasses of univalent function theory. To know much of history, we refer the reader to [11,12,13,14] and so on.
The error function was defined because of the normal curve, and shows up anywhere the normal curve appears. Error function occurs in diffusion which is a part of transport phenomena. It is also useful in biology, mass flow, chemistry, physics and thermomechanics. According to the information at hand, Abramowitz [15] expanded the error function into Maclaurin series of the form
Erf(z)=2√π∫z0e−t2dt=2√π∞∑n=0(−1)nz2n+1(2n+1)n! | (1.9) |
The properties and inequalities of error function were studied by [16] and [4] while the zeros of complementary error function of the form
erfc(z)=1−erf(z)=2√π∫∞ze−t2dt, | (1.10) |
was investigated by [17], see for more details in [18,19] and so on. In recent time, [20,21,22] and [23] applied error functions in numerical analysis and their results are flying in the air.
For f given by [15] and g with the form g(z)=z+b2z2+b3z3+⋯ their Hadamard product (convolution) by f∗g and at is defined as:
(f∗g)(z)=z+∞∑n=2anbnzn | (1.11) |
Let Erf be a normalized analytical function which is obtained from (1.9) and given by
Erf=√πz2erf(z)=z+∞∑n=2(−1)n−1zn(2n−1)(n−1)! | (1.12) |
Therefore, applying a notation (1.11) to (1.1) and (1.12) we obtain
ϵ=A∗Erf={F:F(z)=(f∗Erf)(z)=z+∞∑n=2(−1)n−1anzn(2n−1)(n−1)!,f∈A}, | (1.13) |
where Erf is the class that consists of a single function or Erf. See concept in Kanas et al. [18] and Ramachandran et al. [19].
Babalola [24] introduced and studied the class of λ−pseudo starlike function of order β(0≤β≤1) which satisfy the condition
Re(z(f′(z))λf(z))>β, | (1.14) |
where λ≥1∈ℜ(z∈D) and denoted by ∠λ(β). We observed from (1.14) that putting λ=2, the geometric condition gives the product combination of bounded turning point and starlike function which satisfy
Ref′(z)(z(f′(z))f(z))>β |
Olatunji [25] extended the class ∠λ(β) to ∠βλ(s,t,Φ) which the geometric condition satisfy
Re((s−t)z(f′(z))λf(sz)−f(tz))>β, |
where s,t∈C,s≠t,λ≥1∈ℜ,0≤β<1,z∈D and Φ(z) is the modified sigmoid function. The initial coefficient bounds were obtained and the relevant connection to Fekete-Szegö inequalities were generated. The contributions of authors like Altinkaya and Özkan [26] and Murugusundaramoorthy and Janani [27] and Murugusundaramoorthy et al. [28] can not be ignored when we are talking on λ-pseudo starlike functions.
Inspired by earlier work by [18,19,29]. In this work, the authors employed the approach of [13] to study the coefficient inequalities for pseudo certain subclasses of analytical functions related to petal type region defined by error function. The first few coefficient bounds and the relevant connection to Fekete-Szegö inequalities were obtained for the classes of functions defined. Also note that, the results obtained here has not been in literature and varying of parameters involved will give birth to corollaries.
For the purpose of the main results, the following lemmas and definitions are very necessary.
Lemma 1.1. If p(z)=1+p1z+p2z2+⋯ is a function with positive real part in D, then, for any complex μ,
|p2−μp21|≤2max{1,|2μ−1|} |
and the result is sharp for the functions
p0(z)=1+z1−zorp(z)=1+z21−z2(z∈D). |
Lemma 1.2. [29] Let p∈UP[A,B],−1≤B<A≤1 and of the form p(z)=1+∞∑n=1pnzn. Then, for a complex number μ, we have
|p2−μp21|≤4π2(A−B)max(1,|4π2(B+1)−23+4μ(A−Bπ2)|). | (1.15) |
The result is sharp and the equality in (1.15) holds for the functions
p1(z)=2(A+1)π2(log1+√z1−√z)2+22(B+1)π2(log1+√z1−√z)2+2 |
or
p2(z)=2(A+1)π2(log1+z1−z)2+22(B+1)π2(log1+z1−z)2+2. |
Proof. For h∈P and of the form h(z)=1+∞∑n=1cnzn, we consider
h(z)=1+w(z)1−w(z) |
where w(z) is such that w(0)=0 and |w(z)|<1. It follows easily that
w(z)=h(z)−1h(z)+1=12z+(c22−c214)z2+(c32−c2c12+c318)z3+⋯ | (1.16) |
Now, if ˜p(z)=1+R1z+R2z2+⋯, then from (1.16), one may have,
˜p(w(z))=1+R1w(z)+R2(w(z))2+R3(w(z))3⋯ | (1.17) |
where R1=8π2,R2=163π2, and R3=18445π2, see [30]. Substitute R1,R2 and R3 into (1.17) to obtain
˜p(w(z))=1+4c1π2z+4π2(c2−c216)z2+4π2(c3−c1c23+2c3145)z3+⋯ | (1.18) |
Since p∈UP[A,B], so from relations (1.16), (1.17) and (1.18), one may have,
p(z)=(A+1)˜p(w(z))−(A−1)(B+1)˜p(w(z))−(B−1)=2+(A+1)4π2c1z+(A+1)4π2(c2−c216)z2+⋯2+(B+1)4π2c1z+(B+1)4π2(c2−c216)z2+⋯ |
This implies that,
p(z)=1+2(A−B)c1π2z+2(A−B)π2(c2−c216−2(B−1)c21π2)z2+8(A−B)π2[((B+1)2π4+B+16π2190)c21−(B+1π2+112)c1c2+c34]z3+⋯ | (1.19) |
If p(z)=1+∑∞n=1pnzn, then equating coefficients of z and z2, one may have,
p1=2π2(A−B)c1 |
and
p2=2π2(A−B)(c2−c216−2(B−1)c21π2). |
Now for a complex number μ, consider
p2−μp21=2(A−B)π2[c2−c21(16+2(B+1)π2+2μ(A−B)π2)] |
This implies that
|p2−μp21|=2(A−B)π2|c2−c21(16+2(B+1)π2+2μ(A−B)π2)|. |
Using Lemma 1.1, one may have
|p2−μp21|=4(A−B)π2max{1,|2v−1|}, |
where v=16+2(B+1)π2+2μ(A−B)π2, which completes the proof of the Lemma.
Definition 1.3. A function FϵA is said to be in the class UCV[λ,A,B], −1≤B<A≤1, if and only if,
Re((B−1)(z(F′(z)λ))′F′(z)−(A−1)(B+1)(z(F′(z)λ))′F′(z)−(A+1))>|(B−1)(z(F′(z)λ))′F′(z)−(A−1)(B+1)(z(F′(z)λ))′F′(z)−(A+1)−1|, | (1.20) |
where λ≥1ϵR or equivalently (z(F′(z)λ))′F′(z)ϵUP[A,B].
Definition 1.4. A function FϵA is said to be in the class US[λ,A,B], −1≤B<A≤1, if and only if,
Re((B−1)z(F′(z)λ)F(z)−(A−1)(B+1)z(F′(z)λ)F(z)−(A+1))>|(B−1)z(F′(z)λ)F(z)−(A−1)(B+1)z(F′(z)λ)F(z)−(A+1)−1|, | (1.21) |
where λ≥1ϵR or equivalently z(F′(z)λ)F(z)ϵUP[A,B].
Definition 1.5. A function FϵA is said to be in the class UMα[λ,A,B], −1≤B<A≤1, if and only if,
Re((B−1)[(1−α)z(F′(z)λ)F(z)+α(z(F′(z)λ))′F′(z)]−(A−1)(B+1)[(1−α)z(F′(z)λ)F(z)+α(z(F′(z)λ))′F′(z)]−(A+1))>|(B−1)[(1−α)z(F′(z)λ)F(z)+α(z(F′(z)λ))′F′(z)]−(A−1)(B+1)[(1−α)z(F′(z)λ)F(z)+α(z(F′(z)λ))′F′(z)]−(A+1)−1|, |
where α≥0 and λ≥1ϵR or equivalently (1−α)z(F′(z)λ)f(z)+α(z(f′(z)λ))′f′(z)∈UP[A,B].
In this section, we shall state and prove the main results, and several corollaries can easily be deduced under various conditions.
Theorem 2.1. Let F∈US[λ,A,B], −1≤B<A≤1, and of the form (1.13). Then, for a real number μ, we have
|a3−μa22|≤40(A−B)|1−3λ|π2max{1,|4(B+1)π2−13−2(A−B)(1−2λ)2π2(2(2λ2−4λ+1)−9μ(1−3λ)5)|}. |
Proof. If F∈US[λ,A,B], −1≤B<A≤1, the it follows from relations (1.18), (1.19), and (1.20),
z(F′(z)λ)F(z)=(A+1)˜p(w(z))−(A−1)(B+1)˜p(w(z))−(B−1), |
where w(z) is such that w(0)=0 and ∣w(z)∣<1. The right hand side of the above expression get its series form from (1.13) and reduces to
z(F′(z)λ)F(z)=1+2(A−B)c1π2z+2(A−B)π2(c2−c216−2(B−1)c21π2)z2 |
+8(A−B)π2[((B+1)2π4+B+16π2190)c21−(B+1π2+112)c1c2+c34]z3+⋯. | (2.1) |
If F(z)=z+∑∞n=2(−1)n−1anzn(2n−1)(n−1)!, then one may have
z(F′(z)λ)F(z)=1+1−2λ3a2z+(2λ2−4λ+19a22−1−3λ10a3)z2+⋯ | (2.2) |
From (2.1) and (2.2), comparison of coefficient of z and z2 gives,
a2=6(A−B)(1−2λ)π2c1 | (2.3) |
and
2λ2−4λ+19a22−1−3λ10a3=2(A−B)π2(c2−16c21−2(B+1)π2c21). |
This implies, by using (2.3), that
a3=−20(A−B)(1−3λ)π2[c2−16c21−2(B+1)π2c21−2(2λ2−4λ+1)(A−B)(1−2λ)2π2c21]. |
Now, for a real number μ consider
|a3−μa22|= |
|−20(A−B)(1−3λ)π2(c2−16c21−2(B+1)π2c21)+40(A−B)2(2λ2−4λ+1)(1−2λ)2(1−3λ)π4−36μ(A−B)2c21(1−2λ)2π4| |
=20(A−B)(1−3λ)π2|c2−c21(16+2(B+1)π2−2(A−B)(2λ2−4λ+1)(1−2λ)2π2+9μ(A−B)(1−3λ)5(1−2λ)2π2)| |
=20(A−B)(1−3λ)π2|c2−vc21| |
where v=16+2(B+1)π2−(A−B)(1−2λ)2π2(2(2λ2−4λ+1)−9μ(1−3λ)5).
Theorem 2.2. Let F∈UCV[λ,A,B], −1≤B<A≤1, and of the form (1.13). Then, for a real number μ, we have
|a3−μa22|≤40(A−B)3|1+3λ|π2max{1,|4(B+1)π2−13−2(1+3λ)(A−B)(1+2λ)2π2(λ−27μ20)|} |
Proof. If F∈UCV[λ,A,B], −1≤B<A≤1, then it follows from relations (1.18), (1.19), and (1.21),
(zF′(z)λ)′F′(z)=(A+1)˜p(w(z))−(A−1)(B+1)˜p(w(z))−(B+1), |
where w(z) is such that w(0)=0 and ∣w(z)∣<1. The right hand side of the above expression get its series form from (1.13) and reduces to,
(zF′(z)λ)′F′(z)=1+2(A−B)c1π2z+2(A−B)π2(c2−c216−2(B+1)π2c21)z2+8(A−B)π2[(B+1π4+B+16π2+190)c31−(B+1π2+112)c1c2+c34]z3+⋯ | (2.4) |
If F(z)=z+∑(−1)n−1anzn(2n−1)(n−1)!, then we have,
(zF′(z)λ)′F′(z)=1−2(1+2λ)3a2z+(1+3λ)(3a310+2λ9a22)z2+⋯ | (2.5) |
From (2.4) and (2.5), comparison of coefficients of z and z2 gives,
a2=−3(A−B)c1(1+2λ)π2 | (2.6) |
and
(1+3λ)(3a310+2λ9a22)=2(A−B)π2(c2−c216−2(B+1)c21π2) |
This implies, by using (2.6), that
a3=103[2(A−B)(1+3λ)π2(c2−c216−2(B+1)c21π2)+2λ(A−B)2c21(1+2λ)2π4]. |
Now, for a real number μ, consider
|a3−μa22|=|−20(A−B)3(1+3λ)π2(c2−16c1−2(B+1)π2c21)+20(A−B)2c213(1+2λ)π4−9μ(A−B)2c21(1+2λ)2π4| |
=20(A−B)3(1+3λ)π2|c2−c21(16+2(B+1)π2−λ(1+3λ)(A−B)(1+2λ)2π2+27μ(A−B)(1+3λ)20(1+2λ)2π2)| |
=20(A−B)3(1+3λ)π2|c2−vc21|, |
where
v=16+2(B+1)π2−(1+3λ)(A−B)(1+2λ)2π2(λ−27μ20). |
Theorem 2.3. F∈Mα[λ,A,B], −1≤B<A≤1, α≥0 and of the form (1.13). Then, for a real number μ, we have
|a3−μa22|≤40(A−B)π2|3(λ+α+2αλ)+α−1|max{1,|4(B+1)π2−13−4(A−B)[1−2λ−α(3+2λ)]2π2(2λ2(1+2α)+2λ(3α−2)+1−α−9μ(3(λ+α+2αλ)+α−1)10)|}. |
Proof. Let F∈Mα[λ,A,B], −1≤B<A≤1, α≥0 and of the form (1.13). Then, for a real number μ, we have
(1−α)z(F′(z))λF(z)+α(z(F′(z))λ)′F′(z)=(A+1)˜p(w(z))−(A−1)(B+1)˜p(w(z))−(B−1), | (2.7) |
where w(z) is such that w(z0)=0 and |w(z)|<1. The right hand side of the above expression get its series form from (2.7) and reduces to
(1−α)z(F′(z))λF(z)+α(z(F′(z))λ)′F′(z)=1+2(A−B)Gπ2z+2(A−B)π2(c2−c216−2(B+1)π2c21)z2+... | (2.8) |
If F(z)=z+∑∞n=2(−1)n−1anzn(2n−1)(n−1)!, then one may have
(1−α)z(F′(z))λF(z)+α(z(F′(z))λ)′F′(z)=(1−α)[1+1−2λ3a2z+(2λ2−4λ+19a22−1−3λ10a3)z2+...]+α[1−2(1+2λ)3a2z+(1+3λ)(3a310+2λ9a22)z2+...] | (2.9) |
from (2.8) and (2.9), comparison of coefficients of z and z2 gives
a2=6(A−B)c1[1−2λ−α(3+2λ)]π2 | (2.10) |
and
3(λ+α+2αλ)+α−110a3−2λ2(1+2λ)+α−19a22=2(A−B)π2(c2−c216−2(B+1)π2c21) |
This implies, by using (2.10), that
a3=103(λ+α+2αλ)+α−1[2(A−B)π2(c2−c216−2(B+1)π2c21)+4(A−B)2[2λ2(1+2λ)+2λ(3α−2)+1−α][1−2λ−α(3+2λ)]2π4c21] |
Now, for a real number μ, consider
|a3−μa22|=|103(λ+α+2αλ)+α−1[2(A−B)π2(c2−c216−2(B+1)π2c21)+4(A−B)2[2λ2(1+2λ)+2λ(3α−2)+1−α][1−2λ−α(3+2λ)]2π4c21]−36(A−B)2μG2[1−2λ−α(3+2λ)]2π4| |
=|20(A−B)π(3(λ+α+2αλ)+α−1)|c2−c21[16+2(B+1)π2−2(A−B)[2λ2(1+2α)+2λ(3α−2)+1−α](1−2λ−α(3+2λ))2π2+18μ(A−B)[3(λ+α+2αλ)+α−1]10[1−2λ−α(3+2λ)]2π2 |
=20(A−B)π(3(λ+α+2αλ)+α−1)|c2−vc21|, |
where
v=16+2(B+1)π2−2(A−B)[2λ2(1+2α)+2λ(3α−2)+1−α](1−2λ−α(3+2λ))2π2+18μ(A−B)[3(λ+α+2αλ)+α−1]10[1−2λ−α(3+2λ)]2π2. |
The force applied on certain subclasses of analytical functions associated with petal type domain defined by error function has played a vital role in this work. The results obtained are new and varying the parameters involved in the classes of function defined, these will bring new more results that has not been in existence.
The authors would like to thank the referees for their valuable comments and suggestions.
The authors declare that they have no conflict of interests.
[1] | G. K. Birajdar and V. H. Mankar, Digital image forgery detection using passive techniques: A survey, Digit. Invest., 10(2013), 226–245. |
[2] | B. J. Chen, M. Yu, Q. T. Su, et al., Fractional quaternion cosine transform and its application in color image copy-move forgery detection, Multimed. Tools Appl., (2018), 1–17. |
[3] | C. M. Pun, B. Liu and X. C. Yuan, Multi-scale noise estimation for image splicing forgery detection, J. Vis. Commun. Image Represent., 10(2016), 195–206. |
[4] | B. Mahdian and S. Saic, Using noise inconsistencies for blind image forensics, Image Vis. Comput., 27(2009), 1497–1503. |
[5] | S. Lyu, X. Pan and X. Zhang, Exposing region splicing forgeries with blind local noise estimation, Int. J. Comput. Vis., 110(2014), 202–221. |
[6] | P. Ferrara, T. Bianchi, R. A. De, et al., Image forgery localization via fine-grained analysis of CFA artifacts, IEEE Trans. Inf. Forensic Secur., 10(2013), 226–245. |
[7] | A. E. Dirik and N. Memon, Image tamper detection based on demosaicing artifacts, In: IEEE International Conference on Image Processing, (2009), 1497–1500. |
[8] | E. González, A. Sandoval, L. García, et al., Digital image tamper detection technique based on spectrum analysis of CFA artifacts, Sensors, 18(2018), 2804. |
[9] | Z. Lin, J. He, X. Tang, et al., Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis, Pattern Recognit., 42(2009), 2492–2501. |
[10] | T. Bianchi and A. Piva, Image forgery localization via block-grained analysis of JPEG artifacts, IEEE Trans. Inf. Forensic Secur., 7(2012), 1003–1017. |
[11] | S. M. Ye, Q. Sun and E. C. Chang, Detecting digital image forgeries by measuring inconsistencies of blocking artifact, In: IEEE International Conference on Multimedia and Expo, (2007), 12–15. |
[12] | W. Li, Y. Yuan and N. Yu, Passive detection of doctored JPEG image via block artifact grid extraction, Signal Process., 89(2009), 1821–1829. |
[13] | W. Luo, J. Huang and G. Qiu, JPEG error analysis and its applications to digital image forensics, IEEE Trans. Inf. Forensic Secur., 5(2010), 480–491. |
[14] | F. Huang, J. Huang and Y. Q. Shi, Detecting double JPEG compression with the same quantization matrix, IEEE Trans. Inf. Forensic Secur., 5(2010), 848–856. |
[15] | A. C. Popescu and H. Farid, Exposing digital forgeries by detecting traces of resampling, IEEE Trans. Signal Process., 53(2005), 758–767. |
[16] | H. D. Li, W. Q. Luo, X. Q. Qiu, et al., Image forgery localization via integrating tampering possibility maps, IEEE Trans. Inf. Forensic Secur., 12(2017), 1240–1252. |
[17] | D. Cozzolino, G. Poggi and L. Verdoliva, Recasting residual-based local descriptors as convolutional neural networks: an application to image forgery detection, In: ACM Workshop on Information Hiding and Multimedia Security, (2017), 159–164. |
[18] | Y. Liu, Q. Guan, X. Zhao, et al., Image forgery localization based on multi-scale convolutional neural networks, In: ACM Workshop on Information Hiding and Multimedia Security, (2018), 85–90. |
[19] | J. H. Bappy, A. K. Roy, J. Bunk, et al., Exploiting spatial structure for localizing manipulated image regions, In: IEEE International Conference on Computer Vision, (2017), 4970–4979. |
[20] | Z. Shi, X. Shen and H. Kang, Image manipulation detection and localization based on the dual-domain convolutional veural networks, IEEE Access, 6(2018), 76437–76453. |
[21] | R. Salloum, Y. Ren and C. C. J. Kuo, Image splicing localization using a multi-task gully convolutional network (MFCN), J. Vis. Commun. Image Represent., 51(2018), 201–209. |
[22] | B. Liu and C. M. Pun, Locating splicing forgery by fully convolutional networks and conditional random field, Signal Process.Image Commun., 66(2018), 103–112. |
[23] | B. J. Chen, X. M. Qi, Y. T. Wang, et al., An Improved Splicing Localization Method by Fully Convolutional Networks, IEEE Access, 6(2018), 69472–69480. |
[24] | J. H. Bappy, C. Simons, L. Nataraj, et al., Hybrid LSTM and encoder-decoder architecture for detection of image forgeries, IEEE Trans. Image Process., 28(2019), 3286–3300. |
[25] | T. Parcollet, M. Morchid and G. Linarès, Quaternion convolutional neural networks heterogeneous image processing, preprint, arXiv: 1811.02656. |
[26] | T. A. Ell and S. J. Sangwine, Hypercomplex fourier transforms of color images, IEEE Trans. Image Process., 16(2007), 22–35. |
[27] | B. J. Chen, G. Coatrieux, G. Chen, et al., Full 4-D quaternion discrete Fourier transform based watermarking for color images, Digit. Signal Proc., 28(2014), 106–119. |
[28] | N. Matsui, T. Isokawa, H. Kusamichi, et al., Quaternion neural network with geometrical operators, J. Intell. Fuzzy Syst., 15(2004), 149–164. |
[29] | X. Xu, Z. Guo, C. Song, et al., Multispectral palmprint recognition using a quaternion matrix, Sensors, 12(2012), 4633–4647. |
[30] | B. J. Chen, J. H. Yang, B. Jeon, et al., Kernel quaternion principal component analysis and its application in RGB-D object recognition, Neurocomputing, 266(2017), 293–303. |
[31] | G. L. Xu, X. T. Wang and X. G. Xu, Fractional quaternion Fourier transform, convolution and correlation, Signal Process., 88(2008), 2511–2517. |
[32] | B. J. Chen, C. F. Zhou, B. Jeon, et al., Quaternion discrete fractional random transform for color image adaptive watermarking, Multimed. Tools Appl., 77(2018), 20809–20837. |
[33] | K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, preprint, arXiv: 1409.1556. |
[34] | Q. Cui, S. McIntosh and H. Y. Sun, Identifying materials of photographic images and photorealistic computer generated graphics based on deep CNNs, Comput. Mat. Contin., 55(2018), 229–241. |
[35] | H. Y. Zhao, C. Che, B. Jin, et al., A viral protein identifying framework based on temporal convolutional network, Math. Biosci. Eng., 16(2019), 1709–1717. |
[36] | L. G. Zheng and C. Song, Fast near-duplicate image detection in Riemannian space by a novel hashing scheme, Comput. Mat. Contin., 56(2018), 529–539. |
[37] | K. He, X. Zhang, S. Ren, et al., Deep residual learning for image recognition, In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), (2016), 770–778. |
[38] | X. Zhu, Y. Xu and H. Xu, Quaternion convolutional neural networks, In: European Conference on Computer Vision, (2018), 631–647. |
[39] | C. J. Gaudet and A. S. Maida, Deep quaternion networks, In: IEEE International Joint Conference on Neural Networks, (2018), 1–8. |
[40] | S. Ioffe and C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariate shift, In: International Conference on International Conference on Machine Learning, (2015), 448–456. |
[41] | E. Shelhamer, J. Long and T. Darrell, Fully Convolutional Networks for Semantic Segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 39(2014), 640–651. |
[42] | A. Arnab, S. Jayasumana, S. Zheng, et al., Higher order conditional random fields in deep neural networks, In: European Conference on Computer Vision, (2016), 524–540. |
[43] | Y. H. Zheng, L. Sun, S. F. Wang, et al., Spatially regularized structural support vector machine for robust visual tracking. IEEE Trans. Neural Netw. Learn. Syst., 2018. DOI: 10.1109/tnnls.2018.2855686 |
[44] | L. Sulimowicz, I. Ahmad and A. Aved, Superpixel-enhanced pairwise conditional random field for semantic segmentation, In: IEEE International Conference on Image Processing, (2018), 271–275. |
[45] | P. Kohli and P. H. S. Torr, Robust higher order potentials for enforcing label consistency, Int. J. Comput. Vis., 82(2009), 302–324. |
[46] | J. Dong and W. Wang, CASIA tampered image detection evaluation (TIDE) database, v1.0 and v2.0, Chinese Academy of Sciences, 2011, Available online: http://forensics.idealtest.org/. |
[47] | T. T. Ng and S. F. Chang, A dataset of authentic and spliced image blocks, Columbia University, 2004, Available online: http://www.ee.columbia.edu/ln/dvmm/downloads/. |
[48] | M. Zampoglou, S. Papadopoulos and Y. Kompatsiaris, Large-scale evaluation of splicing localization algorithms for web images, Multimed. Tools Appl., 76(2017), 1–34. |
[49] | F. Xiao, L.Chen, H. Zhu, et al., Anomaly-tolerant network traffic map estimation via noise-immune temporal matrix completion, IEEE J. Sel. Area. Comm., 37(2019), 1192–1204. |
1. | Sheza M. El-Deeb, Luminita-Ioana Cotîrlă, Coefficient Estimates for Quasi-Subordination Classes Connected with the Combination of q-Convolution and Error Function, 2023, 11, 2227-7390, 4834, 10.3390/math11234834 | |
2. | Arzu Akgül, 2024, Chapter 8, 978-981-97-3237-1, 159, 10.1007/978-981-97-3238-8_8 |