Loading [MathJax]/jax/output/SVG/jax.js
Research article Special Issues

Analyzing temporal coherence for deepfake video detection

  • Current facial image manipulation techniques have caused public concerns while achieving impressive quality. However, these techniques are mostly bound to a single frame for synthesized videos and pay little attention to the most discriminatory temporal frequency artifacts between various frames. Detecting deepfake videos using temporal modeling still poses a challenge. To address this issue, we present a novel deepfake video detection framework in this paper that consists of two levels: temporal modeling and coherence analysis. At the first level, to fully capture temporal coherence over the entire video, we devise an efficient temporal facial pattern (TFP) mechanism that explores the color variations of forgery-sensitive facial areas by providing global and local-successive temporal views. The second level presents a temporal coherence analyzing network (TCAN), which consists of novel global temporal self-attention characteristics, high-resolution fine and low-resolution coarse feature extraction, and aggregation mechanisms, with the aims of long-range relationship modeling from a local-successive temporal perspective within a TFP and capturing the vital dynamic incoherence for robust detection. Thorough experiments on large-scale datasets, including FaceForensics++, DeepFakeDetection, DeepFake Detection Challenge, CelebDF-V2, and DeeperForensics, reveal that our paradigm surpasses current approaches and stays effective when detecting unseen sorts of deepfake videos.

    Citation: Muhammad Ahmad Amin, Yongjian Hu, Jiankun Hu. Analyzing temporal coherence for deepfake video detection[J]. Electronic Research Archive, 2024, 32(4): 2621-2641. doi: 10.3934/era.2024119

    Related Papers:

    [1] Dojin Kim, Patcharee Wongsason, Jongkyum Kwon . Type 2 degenerate modified poly-Bernoulli polynomials arising from the degenerate poly-exponential functions. AIMS Mathematics, 2022, 7(6): 9716-9730. doi: 10.3934/math.2022541
    [2] Taekyun Kim, Dae San Kim, Dmitry V. Dolgy, Hye Kyung Kim, Hyunseok Lee . A new approach to Bell and poly-Bell numbers and polynomials. AIMS Mathematics, 2022, 7(3): 4004-4016. doi: 10.3934/math.2022221
    [3] Jongkyum Kwon, Patcharee Wongsason, Yunjae Kim, Dojin Kim . Representations of modified type 2 degenerate poly-Bernoulli polynomials. AIMS Mathematics, 2022, 7(6): 11443-11463. doi: 10.3934/math.2022638
    [4] Sang Jo Yun, Jin-Woo Park . On a generation of degenerate Daehee polynomials. AIMS Mathematics, 2025, 10(5): 12286-12298. doi: 10.3934/math.2025556
    [5] Taekyun Kim, Hye Kyung Kim, Dae San Kim . Some identities on degenerate hyperbolic functions arising from p-adic integrals on Zp. AIMS Mathematics, 2023, 8(11): 25443-25453. doi: 10.3934/math.20231298
    [6] Taekyun Kim, Dae San Kim, Hye Kyung Kim . Some identities involving degenerate Stirling numbers arising from normal ordering. AIMS Mathematics, 2022, 7(9): 17357-17368. doi: 10.3934/math.2022956
    [7] Taekyun Kim, Dae San Kim, Hyunseok Lee, Lee-Chae Jang . A note on degenerate derangement polynomials and numbers. AIMS Mathematics, 2021, 6(6): 6469-6481. doi: 10.3934/math.2021380
    [8] Hye Kyung Kim, Dmitry V. Dolgy . Degenerate Catalan-Daehee numbers and polynomials of order r arising from degenerate umbral calculus. AIMS Mathematics, 2022, 7(3): 3845-3865. doi: 10.3934/math.2022213
    [9] Taekyun Kim, Dae San Kim, Jin-Woo Park . Degenerate r-truncated Stirling numbers. AIMS Mathematics, 2023, 8(11): 25957-25965. doi: 10.3934/math.20231322
    [10] Jung Yoog Kang, Cheon Seoung Ryoo . The forms of (q,h)-difference equation and the roots structure of their solutions with degenerate quantum Genocchi polynomials. AIMS Mathematics, 2024, 9(11): 29645-29661. doi: 10.3934/math.20241436
  • Current facial image manipulation techniques have caused public concerns while achieving impressive quality. However, these techniques are mostly bound to a single frame for synthesized videos and pay little attention to the most discriminatory temporal frequency artifacts between various frames. Detecting deepfake videos using temporal modeling still poses a challenge. To address this issue, we present a novel deepfake video detection framework in this paper that consists of two levels: temporal modeling and coherence analysis. At the first level, to fully capture temporal coherence over the entire video, we devise an efficient temporal facial pattern (TFP) mechanism that explores the color variations of forgery-sensitive facial areas by providing global and local-successive temporal views. The second level presents a temporal coherence analyzing network (TCAN), which consists of novel global temporal self-attention characteristics, high-resolution fine and low-resolution coarse feature extraction, and aggregation mechanisms, with the aims of long-range relationship modeling from a local-successive temporal perspective within a TFP and capturing the vital dynamic incoherence for robust detection. Thorough experiments on large-scale datasets, including FaceForensics++, DeepFakeDetection, DeepFake Detection Challenge, CelebDF-V2, and DeeperForensics, reveal that our paradigm surpasses current approaches and stays effective when detecting unseen sorts of deepfake videos.



    In [1,2], Carlitz initiated study of the degenerate Bernoulli and Euler polynomials and obtained some arithmetic and combinatorial results on them. In recent years, many mathematicians have drawn their attention to various degenerate versions of some old and new polynomials and numbers, namely some degenerate versions of Bernoulli numbers and polynomials of the second kind, Changhee numbers of the second kind, Daehee numbers of the second kind, Bernstein polynomials, central Bell numbers and polynomials, central factorial numbers of the second kind, Cauchy numbers, Eulerian numbers and polynomials, Fubini polynomials, Stirling numbers of the first kind, Stirling polynomials of the second kind, central complete Bell polynomials, Bell numbers and polynomials, type 2 Bernoulli numbers and polynomials, type 2 Bernoulli polynomials of the second kind, poly-Bernoulli numbers and polynomials, poly-Cauchy polynomials, and of Frobenius-Euler polynomials, to name a few [3,14,16,17,18] and the references therein. They have studied those polynomials and numbers with their interest not only in combinatorial and arithmetic properties but also in differential equations and certain symmetric identities [4,5] and references therein, and found many interesting results related to them [12,19,20,21,22,23,24,25,26,27,28]. It is remarkable that studying degenerate versions is not only limited to polynomials but also extended to transcendental functions.

    The Bernoulli polynomials of the second are defined by as follows (see [9,13])

    zlog(1+z)(1+z)x=q=0bq(x)zqq!. (1.1)

    When x=0, bq(0)=bq are called the Bernoulli numbers of the second kind.

    The degenerate exponential function exλ(z) is defined by (see [6,7,8,9,10,11,12,13,14,15,16,17,18,19])

    exλ(z)=(1+λz)xλ,eλ(z)=(1+λz)1λ,λC{0}. (1.2)

    We note that

    exλ(z)=q=0(x)q,λzqq!,(see[4,21]), (1.3)

    where (x)q,λ=x(xλ)(x(q1)λ),(q1), (x)0,λ=1.

    Note that

    limλ0exλ(z)=q=0xqzqq!=exz.

    The degenerate Bernoulli polynomials which are defined by Carlitz's as follows (see [1,2])

    zeλ(z)1exλ(z)=z(1+λz)1λ1(1+λz)xλ=q=0βq(x;λ)zqq!. (1.4)

    At the point x=0, βq(λ)=βq(0;λ) are called the degenerate Bernoulli numbers.

    Note that

    limλ0βq(x;λ)=Bq(x).

    The polylogarithm function is defined by

    Lik(x)=q=1xqqk(kZ,x∣<1),(see[7]). (1.5)

    Note that

    Li1(x)=q=1xqq=log(1x). (1.6)

    The poly-Bernoulli polynomials of the second are defined by (see [13])

    Lik(1ez)log(1+z)(1+z)x=q=0b(k)q(x)zqq!. (1.7)

    In the case when x=0, b(k)q=b(k)q(0) are called the poly-Bernoulli numbers of the second kind.

    The modified degenerate polyexponential function is defined by (see [14])

    Eik,λ(x)=q=1(1)q,λ(q1)!qkxq. (1.8)

    It is noteworthy to mention that

    Ei1,λ(x)=q=1(1)q,λq!xq=eλ(x)1.

    The degenerate poly-Genocchi polynomials which are defined by Kim et al. as follows (see [14])

    2Eik,λ(logλ(1+z))eλ(z)+1exλ(z)=q=0G(k)q,λ(x)zqq!(kZ). (1.9)

    When x=0, G(k)q,λ=G(k)q,λ(0) are called the degenerate poly-Genocchi numbers.

    For λR, Kim-Kim defined the degenerate version of the logarithm function, denoted by logλ(1+t) as follows (see [11])

    logλ(1+z)=q=1λλ1(1)q,1/λzqq!, (1.10)

    being the inverse of the degenerate version of the exponential function eλ(z) as has been shown below

    eλ(logλ(z))=logλ(eλ(z))=z.

    It is noteworthy to mention that

    limλ0logλ(1+z)=q=1(1)q1zqq!=log(1+z).

    The degenerate Daehee polynomials are defined by (see [15])

    logλ(1+z)z(1+z)x=q=0Dq,λ(x)zqq!. (1.11)

    In the case when x=0, Dq,λ=Dq,λ(0) denotes the degenerate Daehee numbers.

    The degenerate Bernoulli polynomials of the second kind which are defined by Kim et al. as follows (see [9])

    zlogλ(1+z)(1+z)x=q=0bq,λ(x)zqq!. (1.12)

    When x=0, bq,λ=bq,λ(0) are called the degenerate Bernoulli numbers of the second kind.

    Note here that limλ0bq,λ(x)=bq(x),(q0).

    The degenerate Stirling numbers of the first kind are defined by

    1k!(logλ(1+z))k=q=kS1,λ(q,k)zqq!(k0),(see[11,12]). (1.13)

    It is noticed that

    limλ0S1,λ(q,k)=S1(q,k),

    are the Stirling numbers of the first kind presented by

    1k!(log(1+z))k=q=kS1(q,k)zqq!(k0),(see[7,17]).

    The degenerate Stirling numbers of the second kind are defined by (see [8])

    1k!(eλ(z)1)k=q=kS2,λ(q,k)zqq!(k0). (1.14)

    It is clear that

    limλ0S2,λ(q,k)=S2(q,k),

    are the Stirling numbers of the second kind specified by

    1k!(ez1)k=q=kS2(q,k)zqq!(k0),(see[128]).

    Motivated by the works of Kim et al. [11,14], in this paper, we study the type 2 degenerate poly-Bernoulli polynomials of the second kind arising from modified degenerate polyexponential function and obtain some related identities and explicit expressions. Also, we establish the type 2 degenerate unipoly-Bernoulli polynomials of the second kind attached to an arithmetic function by using modified degenerate polyexponential function and discuss some properties of them.

    Here, the type 2 degenerate poly-Bernoulli polynomials of the second kind are defined by using the modified degenerate polyexponential function which is called the degenerate poly-Bernoulli polynomials of the second kind as

    Eik,λ(logλ(1+z))logλ(1+z)(1+z)x=j=0Pb(k)j,λ(x)zjj!,(kZ). (2.1)

    When x=0, Pb(k)j,λ=Pb(k)j,λ(0) are called the type 2 degenerate poly-Bernoulli numbers of the second kind.

    Note that

    limλ0Eik,λ(logλ(1+z))logλ(1+z)(1+z)x=j=0limλ0Pb(k)j,λ(x)zjj!
    =Eik(log(1+z))log(1+z)(1+z)x=j=0Pb(k)j(x)zjj!,(kZ), (2.2)

    where Pb(k)j(x) are called the type 2 poly-Bernoulli polynomials of the second kind (see [9]).

    First, we note that

    Eik,λ(logλ(1+z))=q=1(1)q,λ(logλ(1+z))q(q1)!qk
    =q=0(1)q+1,λ(logλ(1+z))q+1(q+1)kq!
    =q=0(1)q+1,λ(q+1)k11(q+1)!(logλ(1+z))q+1
    =q=0(1)q+1,λ(q+1)k1r=q+1S1,λ(r,q+1)zrr!. (2.3)

    By making use of (2.1) and (2.3), we see that

    zlogλ(1+z)(1+z)xEik,λ(logλ(1+z))
    =zlogλ(1+z)(1+z)xq=0(1)q+1,λ(q+1)k1r=qS1,λ(r+1,q+1)r+1zrr!
    =j=0bj,λ(x)zjj!q=0(1)q+1,λ(q+1)k1r=qS1,λ(r+1,q+1)r+1zrr!
    =j=0(jr=0(jr)rq=0(1)q+1,λ(q+1)k1S1,λ(r+1,q+1)r+1bjr,λ(x))zjj!. (2.4)

    Therefore, by (2.3) and (2.4), we obtain the following theorem.

    Theorem 2.1. For kZ and j0, we have

    Pb(k)j,λ(x)=jr=0(jr)rq=0(1)q+1,λ(q+1)k1S1,λ(r+1,q+1)r+1bjr,λ(x).

    Corollary 2.1. Putting k=1 in Theorem 2.1 yields

    Pbj,λ(x)=jr=0(jr)rq=0(1)q+1,λS1,λ(r+1,q+1)r+1bjr,λ(x).

    Let 1kZ. For sC, the function χk,λ(s) is given as

    χk,λ(s)=1Γ(s)0zs1logλ(1+z)Eik,λ(logλ(1+z))dz. (2.5)

    From Eq (2.5), we have

    χk,λ(s)=1Γ(s)0zs1logλ(1+z)Eik,λ(logλ(1+z))dz
    =1Γ(s)10zs1logλ(1+z)Eik,λ(logλ(1+z))dz
    +1Γ(s)1zs1logλ(1+z)Eik,λ(logλ(1+z))dz. (2.6)

    For any sC, the second integral is absolutely convergent and thus, the second term on the r.h.s. vanishes at non-positive integers. That is,

    limsm|1Γ(s)1zs1logλ(1+z)Eik,λ(logλ(1+z))dz|1Γ(m)M=0. (2.7)

    On the other hand, the first integral in Eq (2.7), for (s)>0 can be written as

    1Γ(s)r=0Pb(k)r,λr!1s+r,

    which defines an entire function of s. Thus, we may include that χk,λ(s) can be continued to an entire function of s.

    Further, from (2.6) and (2.7), we obtain

    χk,λ(m)=limsm1Γ(s)10zs1logλ(1+z)Eik,λ(logλ(1+z))dz
    =limsm1Γ(s)10zs1r=0Pb(k)r,λzrr!dz=limsm1Γ(s)r=0Pb(k)r,λs+r1r!
    =+0++0+limsm1Γ(s)1s+mPb(k)m,λm!+0+0+ (2.8)
    =limsm(Γ(1s)sinπsπ)s+mPb(k)m,λm!=Γ(1+m)cos(πm)Pb(k)m,λm!
    =(1)mPb(k)m,λ.

    In view of (2.8), we obtain the following theorem.

    Theorem 2.2. Let k1 and mN{0}, sC, we have

    χk,λ(m)=(1)mPb(k)m,λ.

    Using (1.8), we observe that

    ddxEik,λ(logλ(1+x))=ddxj=1(1)j,λ(logλ(1+x))jjk(j1)!
    =(1+x)λ1logλ(1+x)j=1(1)j,λ(logλ(1+x))jjk1(j1)!=(1+x)λ1logλ(1+x)Eik1,λ(logλ(1+x)). (2.9)

    Thus, by (2.9), for k2, we get

    Eik,λ(logλ(1+x))=x0(1+z)λ1log(1+z)Eik1,λ(logλ(1+z))dz
    =x0(1+z)λ1logλ(1+z)z0(1+z)λ1logλ(1+z)z0(1+z)λ1logλ(1+z)(k2)timesdzdz
    ×Ei1,λ(logλ(1+z))dzdz
    =x0(1+z)λ1logλ(1+z)z0(1+z)λ1logλ(1+z)z0(1+z)λ1logλ(1+z)(k2)timeszdzdz. (2.10)

    From (2.1) and (2.10), we get

    j=0Pb(k)j,λxjj!=Eik,λ(logλ(1+x))logλ(1+x)=1logλ(1+x)
    ×x0(1+z)λ1logλ(1+z)z0(1+z)λ1logλ(1+z)z0(k2)times(1+z)λ1logλ(1+z)zdzdz. (2.11)
    =xlogλ(1+x)q=0q1++qk1=q(qq1,,qk1)
    ×bq1,λ(λ1)q1+1bq2,λ(λ1)q1+q2+1bqk1,λ(λ1)q1++qk1+1xqq!
    =j=0jq=0(jq)q1++qk1=q(qq1,,qk1)bjq,λ
    ×bq1,λ(λ1)q1+1bq2,λ(λ1)q1+q2+1bqk1,λ(λ1)q1++qk1+1xjj!. (2.12)

    Therefore, by (2.12), we obtain the following theorem.

    Theorem 2.3. For jN and kZ, we have

    Pb(k)j,λ=jq=0(jq)q1++qk1=q(qq1,,qk1)bjq,λ
    ×bq1,λ(λ1)q1+1bq2,λ(λ1)q1+q2+1bqk1,λ(λ1)q1++qk1+1.

    Corollary 2.2. Taking k=2 in Theorem 2.3 yields

    Pb(2)j,λ=jq=0(jq)bq,λ(λ1)q+1bjq,λ.

    Replacing z by eλ(z)1 in (2.1), we get

    q=0Pb(k)q,λ(x)(eλ(z)1)qq!=Eik,λ(z)zexλ(z)
    =j=0(x)j,λzjj!r=0(1)r+1,λzr(r+1)kr!=j=0(jr=0(jr)(1)r+1,λ(x)jr,λ(r+1)k)zjj!. (2.13)

    On the other hand,

    q=0Pb(k)q,λ(x)(eλ(z)1)qq!=q=0Pb(k)q,λ(x)j=qS2,λ(j,q)zjj!
    =j=0(jq=0Pb(k)q,λ(x)S2,λ(j,q))zjj!. (2.14)

    In view of (2.13) and (2.14), we get the following theorem.

    Theorem 2.4. For kZ and j0, we have

    jq=0Pb(k)q,λ(x)S2,λ(j,q)=jr=0(jr)(1)r+1,λ(x)jr,λ(r+1)k.

    By using (2.1), we get

    j=1[Pb(k)j,λ(x+1)Pb(k)j,λ(x)]zjj!=Eik,λ(logλ(1+z))logλ(1+z)(1+z)x+1Eik,λ(logλ(1+z))logλ(1+z)(1+z)x
    =zEik,λ(logλ(1+z))logλ(1+z)(1+z)x=(zlogλ(1+z)(1+z)x)(Eik,λ(logλ(1+z)))
    =(j=0bj,λ(x)zjj!)(q=1(1)q,λ(logλ(1+z))q(q1)!qk)
    =(j=0bj,λ(x)zjj!)(q=1(1)q,λ(logλ(1+z))q(q1)!qk)
    =(j=1bj,λ(x)zjj!)(r=1rq=1(1)q,λqk1S1,λ(r,q)zrr!)
    =j=1(jr=1(jr)rq=1(1)q,λqk1S1,λ(r,q)bjr,λ(x))zjj!. (2.15)

    Therefore, by comparing the coefficients on both sides of (2.15), we obtain the following theorem.

    Theorem 2.5. For j0, we have

    Pb(k)j,λ(x+1)Pb(k)j,λ(x)=jr=1(jr)rq=1(1)q,λqk1S1,λ(r,q)bjr,λ(x).

    By making use of (1.3) and (2.1), we have

    j=0Pb(k)j,λ(x+η)zjj!=Eik,λ(logλ(1+z))logλ(1+z)(1+z)x+η
    =Eik,λ(logλ(1+z))logλ(1+z)(1+z)x(1+z)η=(j=0Pb(k)j,λ(x)zjj!)(q=0(η)qzqq!)
    =j=0(jq=0(jq)Pb(k)jq,λ(x)(η)q)zjj!. (2.16)

    Therefore, by Eq (2.16), we obtain the following theorem.

    Theorem 2.6. For j0, we have

    Pb(k)j,λ(x+η)=jq=0(jq)Pb(k)jq,λ(x)(η)q.

    By using (2.1), we have

    Eik,λ(logλ(1+z))logλ(1+z)=j=0Pb(k)j,λzjj!
    Eik,λ(logλ(1+z))=logλ(1+z)j=0Pb(k)j,λzjj!
    Eik,λ(logλ(1+z))z=logλ(1+z)zj=0Pb(k)j,λzjj!
    =(q=0Dq,λtqq!)(j=0Pb(k)j,λzjj!)
    =j=0(jq=0(jq)Pb(k)jq,λDq,λ)zjj!. (2.17)

    On the other hand,

    Eik,λ(logλ(1+z))z=1zq=1(1)q,λ(logλ(1+z))q(q1)!qk
    =1zq=0(1)q+1,λ(logλ(1+z))q+1qk!(q+1)
    =1zq=0(1)q+1,λ(q+1)k11(q+1)!(logλ(1+z))q+1
    =j=0(jq=0(1)q+1,λ(q+1)k1S1,λ(j+1,q+1)j+1)zjj!. (2.18)

    Thus, by equations (2.17) and (2.18), we get the following theorem.

    Theorem 2.7. For j0, we have

    jq=0(jq)Pb(k)jq,λDq,λ=jq=0(1)q+1,λ(q+1)k1S1,λ(j+1,q+1)j+1.

    From (2.1), we have

    n=0Pb(k)j,λ(x)zjj!=Eik,λ(logλ(1+z))logλ(1+z)(1+z)x
    =Eik,λ(logλ(1+z))logλ(1+z)exλ(logλ(1+z))
    =j=0Pb(k)j,λ(x)zjj!q=0(x)q,λr=qS1,λ(r,q)zrr!
    =j=0Pb(k)j,λ(x)zjj!r=0rq=0(x)q,λS1,λ(r,q)zrr!
    =j=0(jr=0(jr)Pb(k)jr,λ(x)q,λS1,λ(r,q))zjj!. (2.19)

    Therefore, by comparing the coefficients on both sides of (2.19), we obtain the following theorem.

    Theorem 2.8. For j0, we have

    Pb(k)j,λ(x)=jr=0(jr)Pb(k)jr,λ(x)q,λS1,λ(r,q).

    Let p be any arithmetic real or complex valued function defined on N. Kim-Kim [7] presented the unipoly function attached to polynomials p(x) as

    uk(x|p)=j=1p(j)jkxn,(kZ). (3.1)

    Moreover,

    uk(x|1)=j=1xjjk=Lik(x),(see[10,14]), (3.2)

    represent the known ordinary polylogarithm function.

    Dolgy and Khan [3] introduced the degenerate unipoly function attached to polynomials p(x) are considered as follows

    uk,λ(x|p)=j=1p(j)(1)j,λxjjk. (3.3)

    We see that

    uk,λ(x|1Γ)=Eik,λ(x),(see[14]) (3.4)

    is the modified degenerate polyexponential function.

    Now, we introduce the degenerate unipoly-Bernoulli polynomials of the second kind attached to polynomials p(x) as

    uk,λ(logλ(1+z)|p)logλ(1+z)(1+z)x=j=0Pb(k)j,λ,p(x)zjj!. (3.5)

    When x=0, Pb(k)j,λ,p=Pb(k)j,λ,p(0) are called the degenerate unipoly-Bernoulli numbers of the second kind attached to p.

    If we take p(j)=1Γ(j). Then, we have

    j=0Pb(k)j,λ,1Γ(x)zjj!=1logλ(1+z)(1+z)xuk,λ(logλ(1+z)|1Γ)
    =1logλ(1+z)(1+z)xq=1(1)q,λ(logλ(1+z))qqk(q1)!. (3.6)

    For k=1, we have

    j=0Pb(1)j,λ,1Γ(x)zjj!=1logλ(1+z)(1+z)xq=1(1)q,λ(logλ(1+z))qq!=zlogλ(1+z)(1+z)x. (3.7)

    Thus, we have

    Pb(1)j,λ,1Γ(x)=bj,λ(x),(j0). (3.8)

    By making use of (1.12) and (3.3), we note that

    uk,λ(logλ(1+z)|p)=q=1p(q)(1)q,λ(logλ(1+z))qqk
    =q=1p(q)(1)q,λ(logλ(1+z))qqkq!q!
    =q=1p(q)(1)q,λq!qk(logλ(1+z))qq!
    =q=1p(q)(1)q,λq!qkr=qS1,λ(r,q)zrr!
    =r=1(rq=1p(q)(1)q,λq!qkS1,λ(r,q))zrr!.

    Thus, we have the required result.

    Lemma 3.1. For kZ, we have

    uk,λ(logλ(1+z)|p)=r=1(rq=1p(q)(1)q,λq!qkS1,λ(r,q))zrr!.

    Recalling from (3.5), we have

    j=0Pb(k)j,λ,p(x)zjj!=1logλ(1+z)(1+z)xuk,λ(logλ(1+z)|p)
    =1logλ(1+z)(1+z)xq=1(1)q,λp(q)qk(logλ(1+z))q
    =1logλ(1+z)(1+z)xq=0(1)q+1,λp(q+1)(q+1)k(logλ(1+z))q+1
    =1logλ(1+z)(1+z)xq=0(1)q+1,λp(q+1)(q+1)!(q+1)kr=q+1Sr,λ(r,q+1)zrr!
    =zlogλ(1+z)(1+z)xq=0(1)q+1,λp(q+1)(q+1)!(q+1)kr=qS1,λ(r+1,q+1)r+1zrr!
    =j=0bj,λ(x)zjj!r=0(rq=0(1)q+1,λp(q+1)(q+1)!(q+1)kS1,λ(r+1,q+1)r+1)zrr!
    =j=0(jr=0rq=0(jr)(1)q+1,λp(q+1)(q+1)!(q+1)kS1,λ(r+1,q+1)r+1bjr,λ(x))zjj!. (3.9)

    Therefore, by comparing the coefficients on both sides of (3.9), we obtain the following theorem.

    Theorem 3.1. For j0 and kZ. Then

    Pb(k)j,λ,p(x)=jr=0rq=0(jr)(1)q+1,λp(q+1)(q+1)!(q+1)kS1,λ(r+1,q+1)r+1bjr,λ(x).

    Moreover,

    Pb(k)j,λ,1Γ(x)=jr=0rq=0(jr)bjr,λ(x)(q+1)k1S1,λ(r+1,q+1)r+1.

    Using (3.5), we have

    j=0Pb(k)j,λ,p(x)zjj!=1logλ(1+z)uk,λ(logλ(1+z)|p)(1+z)x
    =uk,λ(logλ(1+z)|p)logλ(1+z)j=0(x)jzjj!
    =i=0Pb(k)i,λ,pzii!j=0(x)jzjj!
    =j=0(ji=0(ji)Pb(k)i,λ,p(x)ji)zjj!. (3.10)

    Upon comparing the coefficients on both sides of Eq (3.10), we get the following theorem.

    Theorem 3.2. For j0 and kZ. Then

    Pb(k)j,λ,p(x)=ji=0(ji)Pb(k)i,λ,p(x)ji.

    By making use of (1.11), (1.12) and (3.5), we have

    j=0Pb(k)j,λ,pzjj!=1logλ(1+z)uk(logλ(1+z)|p)
    =1logλ(1+z)q=1(1)q,λp(q)qk(logλ(1+z))q
    =q=0(1)q,λp(q+1)(q+1)k(logλ(1+z))q+1
    =zlogλ(1+z)logλ(1+z)zq=0(1)q+1,λp(q+1)q!(q+1)k(logλ(1+z))qq!
    =j=0Dj,λzjj!i=0bi,λzii!q=0(1)q+1,λp(q+1)q!(q+1)kr=qS1,λ(r,q)zrr!
    =j=0Dj,λzjj!i=0bi,λzii!r=0rq=0(1)q+1,λp(q+1)q!(q+1)kS1,λ(r,q)zrr!
    =j=0ji=0(ji)Dji,λbi,λzjj!r=0rq=0(1)q+1,λp(q+1)q!(q+1)kS1,λ(r,q)zrr!
    =j=0(jr=0rq=0jri=0(jri)(jr)Djir,λbi,λ(1)q+1,λp(q+1)q!(q+1)kS1,λ(r,q))zjj!. (3.11)

    Thus, by comparing the coefficients on both sides of (3.11), we obtain the following theorem.

    Theorem 3.3. For j0 and kZ. Then

    Pb(k)j,λ,p=jr=0rq=0jri=0(jri)(jr)Djir,λbi,λ(1)q+1,λp(q+1)q!(q+1)kS1,λ(r,q).

    In this section, certain numerical computations are done to calculate certain zeros of the degenerate poly-Bernoulli polynomials of the second kind and show some graphical representations. The first five members of Pb(k)j,λ(x) are calculated and given as:

    Pb(k)0,λ(x)=1,Pb(k)1,λ(x)=12+x18log3log818log3,Pb(k)2,λ(x)=12+x2+1081(log3)2+18log3x4log3log818log3xlog814log3,Pb(k)3,λ(x)=14+2x3x22+x3516(log3)31027(log3)2+10x27(log3)214log3+3x4log33x28log3+log8116log33x2log818log3,Pb(k)4,λ(x)=126x+8x24x3+x4+176125(log3)4+158(log3)35x4(log3)3+11081(log3)220x9(log3)2+20x227(log3)2+34log311x4log3+9x24log3x32log3log818log3+3x2log814log3x3log812log3.

    To show the behavior of Pb(k)j,λ(x), we display the graph Pb(k)j,λ(x) for k=4 and λ=3, this graph is presented in Figure 1.

    Figure 1.  Graph of Pb(k)j,λ(x).

    Next, the approximate solutions of Pb(k)j,λ(x)=0 when k=4 and λ=3, are calculated and listed in Table 1.

    Table 1.  Approximate solutions of Pb(k)j,λ(x)=0.
    j Real zeros Complex zeros
    1 0.11378 -
    2 0.212959,1.0146 -
    3 0.468628,0.788431,2.08428 -
    4 2.27482,3.00114 0.5895820.515659i,0.589582+0.515659i
    5 4.09322 0.4709670.872952i,0.470967+0.872952i,
    2.766870.464588i,2.76687+0.464588i
    6 4.47754,4.94352 0.2705091.2071i,0.270509+1.2071i
    2.86031.06554i,2.8603+1.06554i
    7 6.12953 0.004072371.52417i,0.00407237+1.52417i,
    2.85441.67974i,2.8544+1.67974i
    4.983140.749479i,4.98314+0.749479i
    8 - 0.3448721.82511i,0.344872+1.82511i,
    2.75372.30093i,2.7537+2.30093i,
    5.212621.46596i,5.21262+1.46596i,
    6.833670.248836i,6.83367+0.248836i

     | Show Table
    DownLoad: CSV

    The zeros of Pb(k)j,λ(x) for λC,j=12 are plotted in Figure 2.

    Figure 2.  Zeros of Pb(k)12,λ(x).

    The stacking structure of approximate zeros of Pb(k)j,λ(x)=0 for λ=4,j=1,2,...,12 is given in Figure 3.

    Figure 3.  Stacking structure of zeros Pb(k)j,λ(x).

    In this article, we introduced the type 2 degenerate poly-Bernoulli polynomials of the second kind and derived many related interesting properties. Furthermore, we defined the degenerate unipoly Bernoulli polynomials of the second kind and established some considerable results. Finally, certain related beautiful zeros and graphs are shown.

    The authors would like to express the gratitude to Deanship of Scientific Research at King Khalid University, Saudi Arabia for providing funding research group under the research grant number R G P.1/162/42.

    The authors declare no conflict of interest.



    [1] M. Kowalski, Deepfakes. Available from: https://www.github.com/MarekKowalski/FaceSwap/.
    [2] K. Liu, I. Perov, D. Gao, N. Chervoniy, W. Zhou, W. Zhang, Deepfacelab: integrated, flexible and extensible face-swapping framework, Pattern Recognit., 141 (2023), 109628. https://doi.org/10.1016/j.patcog.2023.109628 doi: 10.1016/j.patcog.2023.109628
    [3] D. Afchar, V. Nozick, J. Yamagishi, I. Echizen, MesoNet: a compact facial video forgery detection network, in 2018 IEEE International Workshop on Information Forensics and Security (WIFS), (2018), 1–7. https://doi.org/10.1109/WIFS.2018.8630761
    [4] F. Matern, C. Riess, M. Stamminger, Exploiting visual artifacts to expose deepfakes and face manipulations, in 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW), (2019), 83–92. https://doi.org/10.1109/WACVW.2019.00020
    [5] Y. Qian, G. Yin, L. Sheng, Z. Chen, J. Shao, Thinking in frequency: face forgery detection by mining frequency-aware clues, in ECCV 2020: Computer Vision – ECCV 2020, Springer-Verlag, (2020), 86–103. https://doi.org/10.1007/978-3-030-58610-2_6
    [6] H. Liu, X. Li, W. Zhou, Y. Chen, Y. He, H. Xue, et al., Spatial-phase shallow learning: rethinking face forgery detection in bfrequency domain, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2021), 772–781.
    [7] S. Chen, T. Yao, Y. Chen, S. Ding, J. Li, R. Ji, Local relation learning for face forgery detection, in Proceedings of the AAAI Conference on Artificial Intelligence, 35 (2021), 1081–1088. https://doi.org/10.1609/aaai.v35i2.16193
    [8] Q. Gu, S. Chen, T. Yao, Y. Chen, S. Ding, R. Yi, Exploiting fine-grained face forgery clues via progressive enhancement learning, in Proceedings of the AAAI Conference on Artificial Intelligence, 36 (2022), 735–743. https://doi.org/10.1609/aaai.v36i1.19954
    [9] X. Li, Y. Lang, Y. Chen, X. Mao, Y. He, S. Wang, et al., Sharp multiple instance learning for DeepFake video detection, in Proceedings of the 28th ACM International Conference on Multimedia, (2020), 1864–1872. https://doi.org/10.1145/3394171.3414034
    [10] Z. Gu, Y. Chen, T. Yao, S. Ding, J. Li, F. Huang, et al., Spatiotemporal inconsistency learning for DeepFake video detection, in Proceedings of the 29th ACM International Conference on Multimedia, (2021), 3473–3481. https://doi.org/10.1145/3474085.3475508
    [11] S. A. Khan, H. Dai, Video transformer for deepfake detection with incremental learning, in Proceedings of the 29th ACM International Conference on Multimedia, (2021), 1821–1828. http://doi.org/10.1145/3474085.3475332
    [12] D. H. Choi, H. J. Lee, S. Lee, J. U. Kim, Y. M. Ro, Fake video detection with certainty-based attention network, in 2020 IEEE International Conference on Image Processing (ICIP), (2020), 823–827. http://doi.org/10.1109/ICIP40778.2020.9190655
    [13] E. Sabir, J. Cheng, A. Jaiswal, W. Abdalmageed, I. Masi, P. Natarajan, Recurrent convolutional strategies for face manipulation detection in videos, Interfaces (GUI), (2019), 80–87.
    [14] A. Chintha, B. Thai, S. J. Sohrawardi, K. Bhatt, A. Hickerson, M. Wright, et al., Recurrent convolutional structures for audio spoof and video deepfake detection, IEEE J. Sel. Top. Signal Process., 14 (2020), 1024–1037. http://doi.org/10.1109/JSTSP.2020.2999185 doi: 10.1109/JSTSP.2020.2999185
    [15] A. Haliassos, K. Vougioukas, S. Petridis, M. Pantic, Lips don't lie: a generalisable and robust approach to face forgery detection, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2021), 5039–5049.
    [16] Y. Zheng, J. Bao, D. Chen, M. Zeng, F. Wen, Exploring temporal coherence for more general video face forgery detection, in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), (2021), 15044–15054.
    [17] Z. Gu, Y. Chen, T. Yao, S. Ding, J. Li, L. Ma, Delving into the local: dynamic inconsistency learning for DeepFake video detection, in Proceedings of the AAAI Conference on Artificial Intelligence, 36 (2022), 744–752. http://doi.org/10.1609/aaai.v36i1.19955
    [18] X. Zhao, Y. Yu, R. Ni, Y. Zhao, Exploring complementarity of global and local spatiotemporal information for fake face video detection, in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2022), 2884–2888. http://doi.org/10.1109/ICASSP43922.2022.9746061
    [19] R. Shao, T. Wu, Z. Liu, Detecting and recovering sequential DeepFake manipulation, in ECCV 2022: Computer Vision – ECCV 2022, Springer-Verlag, (2022), 712–728. http://doi.org/10.1007/978-3-031-19778-9_41
    [20] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, et al., An image is worth 16 x 16 words: transformers for image recognition at scale, preprint, arXiv: 2010.11929.
    [21] A. Arnab, M. Dehghani, G. Heigold, C. Sun, M. Lučić, C. Schmid, ViViT: a video vision transformer, in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), (2021), 6836–6846.
    [22] Y. Zhang, X. Li, C. Liu, B. Shuai, Y. Zhu, B. Brattoli, et al., VidTr: Video transformer without convolutions, in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), (2021), 13577–13587.
    [23] L. He, Q. Zhou, X. Li, L. Niu, G. Cheng, X. Li, et al., End-to-end video object detection with spatial-temporal transformers, in Proceedings of the 29th ACM International Conference on Multimedia, (2021), 1507–1516. http://doi.org/10.1145/3474085.3475285
    [24] Z. Xu, D. Chen, K. Wei, C. Deng, H. Xue, HiSA: Hierarchically semantic associating for video temporal grounding, IEEE Trans. Image Process., 31 (2022), 5178–5188. http://doi.org/10.1109/TIP.2022.3191841 doi: 10.1109/TIP.2022.3191841
    [25] O. de Lima, S. Franklin, S. Basu, B. Karwoski, A. George, Deepfake detection using spatiotemporal convolutional networks, preprint, arXiv: 2006.14749.
    [26] D. Güera, E. J. Delp, Deepfake video detection using recurrent neural networks, in 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), (2018), 1–6. http://doi.org/10.1109/AVSS.2018.8639163
    [27] I. Masi, A. Killekar, R. M. Mascarenhas, S. P. Gurudatt, W. AbdAlmageed, Two-branch recurrent network for isolating deepfakes in videos, in ECCV 2020: Computer Vision – ECCV 2020, Springer-Verlag, (2020), 667–684. http://doi.org/10.1007/978-3-030-58571-6_39
    [28] Y. Yu, R. Ni, Y. Zhao, S. Yang, F. Xia, N. Jiang, et al., MSVT: Multiple spatiotemporal views transformer for DeepFake video detection, IEEE Trans. Circuits Syst. Video Technol., 33 (2023), 4462–4471. http://doi.org/10.1109/TCSVT.2023.3281448 doi: 10.1109/TCSVT.2023.3281448
    [29] H. Cheng, Y. Guo, T. Wang, Q. Li, X. Chang, L. Nie, Voice-face homogeneity tells deepfake, ACM Trans. Multimedia Comput. Commun. Appl., 20 (2023), 1–22. http://doi.org/10.1145/3625231 doi: 10.1145/3625231
    [30] W. Yang, X. Zhou, Z. Chen, B. Guo, Z. Ba, Z. Xia, et al., AVoiD-DF: Audio-visual joint learning for detecting deepfake, IEEE Trans. Inf. Forensics Secur., 18 (2023), 2015–2029. http://doi.org/10.1109/TIFS.2023.3262148 doi: 10.1109/TIFS.2023.3262148
    [31] M. Liu, J. Wang, X. Qian, H. Li, Audio-visual temporal forgery detection using embedding-level fusion and multi-dimensional contrastive loss, IEEE Trans. Circuits Syst. Video Technol., 2023. http://doi.org/10.1109/TCSVT.2023.3326694
    [32] Q. Yin, W. Lu, B. Li, J. Huang, Dynamic difference learning with spatio–temporal correlation for deepfake video detection, IEEE Trans. Inf. Forensics Secur., 18 (2023), 4046–4058. http://doi.org/10.1109/TIFS.2023.3290752 doi: 10.1109/TIFS.2023.3290752
    [33] Y. Wang, C. Peng, D. Liu, N. Wang, X. Gao, Spatial-temporal frequency forgery clue for video forgery detection in VIS and NIR scenario, IEEE Trans. Circuits Syst. Video Technol., 33 (2023), 7943–7956. http://doi.org/10.1109/TCSVT.2023.3281475 doi: 10.1109/TCSVT.2023.3281475
    [34] D. E. King, Dlib-ml: A machine learning toolkit, J. Mach. Learn. Res., 10 (2009), 1755–1758. Available from: http://www.jmlr.org/papers/volume10/king09a/king09a.pdf.
    [35] A. Rossler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, M. Niessner, FaceForensics++: Learning to detect manipulated facial images, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), (2019), 1–11. http://doi.org/10.1109/ICCV.2019.00009
    [36] E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, P. Luo, SegFormer: Simple and efficient design for semantic segmentation with transformers, in Advances in Neural Information Processing Systems, 34 (2021), 12077–12090.
    [37] W. Wang, E. Xie, X. Li, D. P. Fan, K. Song, D. Liang, et al., Pyramid vision transformer: a versatile backbone for dense prediction without convolutions, in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), (2021), 568–578.
    [38] X. Chu, Z. Tian, B. Zhang, X. Wan, C. Shen, Conditional positional encodings for vision transformers, preprint, arXiv: 2102.10882.
    [39] M. A. Islam, S. Jia, N. D. B. Bruce, How much position information do convolutional neural networks encode? preprint, arXiv: 2001.08248.
    [40] N. Dufour, A. Gully, P. Karlsson, A. V. Vorbyov, T. Leung, J. Childs, et al., Contributing data to Deepfake detection research by Google Research & Jigsaw, 2019. Available from: http://blog.research.google/2019/09/contributing-data-to-deepfake-detection.html.
    [41] B. Dolhansky, J. Bitton, B. Pflaum, J. Lu, R. Howes, M. Wang, et al., The DeepFake detection challenge (DFDC) dataset, preprint, arXiv: 2006.07397.
    [42] Y. Li, X. Yang, P. Sun, H. Qi, S. Lyu, Celeb-DF: A large-scale challenging dataset for DeepFake forensics, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2020), 3207–3216.
    [43] L. Jiang, R. Li, W. Wu, C. Qian, C. C. Loy, DeeperForensics-1.0: A large-scale dataset for real-world face forgery detection, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2020), 2889–2898.
    [44] A. P. Bradley, The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognit., 30 (1997), 1145–1159. http://doi.org/10.1016/S0031-3203(96)00142-2 doi: 10.1016/S0031-3203(96)00142-2
    [45] P. Micikevicius, S. Narang, J. Alben, G. F. Diamos, E. Elsen, D. Garcia, et al., Mixed precision training, preprint, arXiv: 1710.03740.
    [46] Z. Zhang, M. R. Sabuncu, Generalized cross entropy loss for training deep neural networks with noisy labels, in Advances in Neural Information Processing Systems, 31 (2018).
    [47] L. van der Maaten, G. Hinton, Visualizing data using t-SNE, J. Mach. Learn. Res., 9 (2008), 2579–2605.
    [48] I. Radosavovic, R. P. Kosaraju, R. Girshick, K. He, P. Dollár, Designing network design spaces, in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2020), 10425–10433. http://doi.org/10.1109/CVPR42600.2020.01044
    [49] Z. Liu, H. Hu, Y. Lin, Z. Yao, Z. Xie, Y. Wei, et al., Swin transformer v2: Scaling up capacity and resolution, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2022), 12009–12019.
    [50] H. Bao, L. Dong, S. Piao, F. Wei, BEiT: BERT pre-training of image transformers, preprint, arXiv: 2106.08254.
    [51] W. Yu, M. Luo, P. Zhou, C. Si, Y. Zhou, X. Wang, et al., Metaformer is actually what you need for vision, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2022), 10819–10829.
  • This article has been cited by:

    1. Ghulam Muhiuddin, Waseem A. Khan, Deena Al-Kadi, Some Identities of the Degenerate Poly-Cauchy and Unipoly Cauchy Polynomials of the Second Kind, 2022, 132, 1526-1506, 763, 10.32604/cmes.2022.017272
    2. Dojin Kim, Patcharee Wongsason, Jongkyum Kwon, Type 2 degenerate modified poly-Bernoulli polynomials arising from the degenerate poly-exponential functions, 2022, 7, 2473-6988, 9716, 10.3934/math.2022541
    3. Ghulam Muhiuddin, Waseem Ahmad Khan, Jihad Younis, Yuan Yi, Construction of Type 2 Poly-Changhee Polynomials and Its Applications, 2021, 2021, 2314-4785, 1, 10.1155/2021/7167633
    4. Maryam Salem Alatawi, Waseem Ahmad Khan, New Type of Degenerate Changhee–Genocchi Polynomials, 2022, 11, 2075-1680, 355, 10.3390/axioms11080355
    5. Waseem A. Khan, Jihad Younis, Mohd Nadeem, Construction of partially degenerate Laguerre–Bernoulli polynomials of the first kind, 2022, 30, 2769-0911, 362, 10.1080/27690911.2022.2079641
    6. Shahid Ahmad Wani, Ibtehal Alazman, Badr Saad T. Alkahtani, Certain Properties and Applications of Convoluted Δh Multi-Variate Hermite and Appell Sequences, 2023, 15, 2073-8994, 828, 10.3390/sym15040828
    7. Ibtehal Alazman, Badr Saad T. Alkahtani, Shahid Ahmad Wani, Certain Properties of Δh Multi-Variate Hermite Polynomials, 2023, 15, 2073-8994, 839, 10.3390/sym15040839
    8. Azhar Iqbal, Waseem A. Khan, Mohd Nadeem, 2023, Chapter 34, 978-981-19-9857-7, 411, 10.1007/978-981-19-9858-4_34
    9. Waseem Ahmad Khan, Ugur Duran, Jihad Younis, Cheon Seoung Ryoo, On some extensions for degenerate Frobenius-Euler-Genocchi polynomials with applications in computer modeling, 2024, 32, 2769-0911, 10.1080/27690911.2023.2297072
    10. Azhar Iqbal, Waseem A. Khan, 2023, Chapter 6, 978-981-19-9857-7, 59, 10.1007/978-981-19-9858-4_6
    11. Mohra Zayed, Shahid Ahmad Wani, A Study on Generalized Degenerate Form of 2D Appell Polynomials via Fractional Operators, 2023, 7, 2504-3110, 723, 10.3390/fractalfract7100723
    12. Mohra Zayed, Shahid Wani, Exploring the versatile properties and applications of multidimensional degenerate Hermite polynomials, 2023, 8, 2473-6988, 30813, 10.3934/math.20231575
    13. Noor Alam, Waseem Ahmad Khan, Serkan Araci, Hasan Nihal Zaidi, Anas Al Taleb, Evaluation of the Poly-Jindalrae and Poly-Gaenari Polynomials in Terms of Degenerate Functions, 2023, 15, 2073-8994, 1587, 10.3390/sym15081587
    14. Musawa Yahya Almusawa, Exploring the Characteristics of Δh Bivariate Appell Polynomials: An In-Depth Investigation and Extension through Fractional Operators, 2024, 8, 2504-3110, 67, 10.3390/fractalfract8010067
    15. Lingling Luo, Yuankui Ma, Taekyun Kim, Hongze Li, Some identities on degenerate poly-Euler polynomials arising from degenerate polylogarithm functions, 2023, 31, 2769-0911, 10.1080/27690911.2023.2257369
    16. Shahid Ahmad Wani, Two-iterated degenerate Appell polynomials: properties and applications, 2024, 31, 2576-5299, 83, 10.1080/25765299.2024.2302502
    17. Waseem A. Khan, Azhar Iqbal, Mohd Nadeem, 2023, Chapter 50, 978-981-19-9857-7, 589, 10.1007/978-981-19-9858-4_50
    18. Waseem Ahmad Khan, 2024, 2900, 0094-243X, 020001, 10.1063/5.0207187
    19. Lingling Luo, Yuankui Ma, Taekyun Kim, Rongrong Xu, Series involving degenerate harmonic numbers and degenerate Stirling numbers, 2024, 32, 2769-0911, 10.1080/27690911.2023.2297045
    20. Awatif Muflih Alqahtani, Saleem Yousuf, Shahid Ahmad Wani, Roberto S. Costas-Santos, Investigating Multidimensional Degenerate Hybrid Special Polynomials and Their Connection to Appell Sequences: Properties and Applications, 2024, 13, 2075-1680, 859, 10.3390/axioms13120859
    21. Shahid Ahmad Wani, Tabinda Nahid, Ramírez William, Mdi Begum Jeelani, On a new family of degenerate-Sheffer polynomials and related hybrid forms via generating function, 2025, 74, 0009-725X, 10.1007/s12215-025-01228-2
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2565) PDF downloads(123) Cited by(3)

Figures and Tables

Figures(6)  /  Tables(6)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog