Exploring Specular Reflection Inconsistency for Generalizable Face Forgery Detection
Authors: Hongyan Fei
Zexi Jia
Chuanwei Huang
Jinchao Zhang
Jie Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: face forgery detection
illumination separation
specular reflection analysis
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose SRI-Net, which detects deepfakes by exploiting inconsistencies in face specular reflection that are difficult to replicate accurately due to its parametric complexity and nonlinear formulation.

Abstract: Detecting deepfakes has become increasingly challenging as forgery faces synthesized by AI-generated methods, particularly diffusion models, achieve unprecedented quality and resolution. Existing forgery detection approaches relying on spatial and frequency features demonstrate limited efficacy against high-quality, entirely synthesized forgeries. In this paper, we propose a novel detection method grounded in the observation that facial attributes governed by complex physical laws and multiple parameters are inherently difficult to replicate. Specifically, we focus on illumination, particularly the specular reflection component in the Phong illumination model, which poses the greatest replication challenge due to its parametric complexity and nonlinear formulation. We introduce a fast and accurate face texture estimation method based on Retinex theory to enable precise specular reflection separation. Furthermore, drawing from the mathematical formulation of specular reflection, we posit that forgery evidence manifests not only in the specular reflection itself but also in its relationship with corresponding face texture and direct light. To address this issue, we design the Specular-Reflection-Inconsistency-Network (SRI-Net), incorporating a two-stage cross-attention mechanism to capture these correlations and integrate specular reflection related features with image features for robust forgery detection. Experimental results demonstrate that our method achieves superior performance on both traditional deepfake datasets and generative deepfake datasets, particularly those containing diffusion-generated forgery faces.

Published as a conference paper at ICLR 2026
EXPLORING SPECULAR REFLECTION INCONSISTENCY FOR GENERALIZABLE FACE FORGERY DETECTION
Hongyan Fei!.?
Zexi Jia
Chuanwei Huang" 1.2
Jinchao Zhang
Jie Zhou?
School of Intelligence Science and Technology, Peking University
State Key Laboratory of General Artificial Intelligence, Peking University 3 WeChat AI, Tencent Inc Senryaaffiietu.... {zexi jia, dayerzhang, withtommhouu http://ccoo@@eeeeeeeeeeee Stencent.com "Coresponding author.
edu. cn huangewêpku. en
ABSTRACT
Detecting deepfakes has become increasingly challenging as forgery faces synthe- sized by Al-generated methods, particularly diffusion models, achieve unprece- dented quality and resolution. Existing forgery detection approaches relying spatial and frequency features demonstrate limited efficacy against high-quality, entirely synthesized forgeries. In this paper, we propose novel detection method grounded the observation that facial attributes governed by complex physical laws and multiple parameters inherently difficult to replicate. Specifically, we focus on illumination, particularly the specular reflection component in the Phong illumination model, which poses the greatest replication challenge due its parametric complexity and nonlinear formulation. We introduce a fast and accurate face texture estimation method based on Retinex theory to enable pre- cise specular reflection separation. Furthermore, drawing from the mathemati- cal formulation of specular reflection, we posit that forgery evidence manifests not only in the specular reflection itself but also in its relationship with cor- responding face texture and direct light. To address this issue, we design the cenffecciion-nnosisteeeee
(SRI-Net), incorporating two-stage cross-attention mechanism to capture these correlations and integrate specular re- flection related features with image features for robust forgery detection. Experi- mental results demonstrate that our method achieves superior performance on both traditional deepfake datasets and generative deepfake datasets, particularly those containing diffusion-generated forgery faces.
INTRODUCTION
With the rapid advancement of face manipulation techniques, generating highly realistic forgery face images and videos has become increasingly effortless (Rombach et al. [002 Chen et al. 22225. and the potential abuse of this technology raises significant security concerns. Particularly with the rise of Al-generation tools such as diffusion models, which enable more sophisticated, realistic, and higher-resolution entire synthesized forgeries, making the challenge of face forgery detection
becomes even harder (Kawar et al. 2023 Rosberg et al. 2023 Kim et al. 2025).
Current face forgery detection methods primarily identify forgery evidence by analyzing discrimi- native features extracted from the spatial (Li et al. 2020a; Cao et al.) (2022) or frequency (Tan al. 2024) domains of face images, well as leveraging pre-trained features (Cui et al. (025) from mod- els such as CLIP (Radford et al. 2021). However, these approaches face significant limitations as texture inconsistencies in contemporary forgery faces, particularly those entirely synthesized by gen- erative models, become increasingly subtle. Moreover, forgery evidence from different generation methods may manifest in distinct frequency bands, while pre-trained features lack domain-specifer knowledge and interpretability. These factors collectively contribute to the declining detection per- formance of existing methods when confronted with sophisticated synthetic faces.

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Figure 1: The visualization of (a) Spatial-based face forgery detection methods, (b) Frequency- -base face forgery detection methods and (c) Our proposed Specular- Specular-Reflectionn werprettoonnnooosssseeey-eeeeeee -Newook (SRI-Net). SRI-Net analyzes that specular reflection is difficult replicate based on the math- ematical form of the general Phong illumination model and contains generalizable forgery evidence.
In this paper, propose approach to localize forgery evidence from the perspective of face generation. Specifically, we posit that despite the remarkable realism achieved by state-of-the-art forgery techniques, they remain constrained by fundamental principle: attributes governed by more estimated parameters and more complex physical laws are inherently more challenging to replicate accurately. Illumination represents a particularly complex physical phenomenon, as is simultaneously governed by 2D local texture statistics, 3D global environmental conditions, and illumination model constraints, making it a promising avenue for investigation. As illustrated in Fig. [ (c), under the Phong illumination model, face illumination comprises ambient light, direct light, and specular reflection. The mathematical representation of the specular reflection component involves more parameters and exhibits stronger nonlinearity, making it inherently more difficult to accurately replicate.
Based on this analysis, hypothesize that specular reflection contains more generalizable forgery evidence. In this paper, we introduce the 3D Morphable Model (3DMM) (Blanz & Vetter (003) and computer graphics rendering algorithms
achieve illumination/eexture separation and specu- lar reflection extraction. However, since the widely used Basel Face Model (BFM) based texture estimation (Paysan al., 2009) cannot capture fine-grained facial textures, the extracted specular reflection component inevitably contains erroneous details. To address this limitation, we propose faster and more accurate face texture estimation method based Retinex theory (Land & Mc- Cann 197I) to replace the BFM model. Under the constraints of Retinex based texture, we achieve more precise extraction of specular reflection. Furthermore, according the analytical expression of specular reflection in the Phong illumination model, its estimation depends on both direct light inten- sity and reflective material properties. Since human faces share similar skin properties, the relative material characteristics can be approximated through face texture. Consequently, forgery evidence should manifest not only in the specular reflection component itself but also in its relationships with direct light and face texture. To exploit these relationships, design the Specular-Retee Reflection- mnconsistency-Neteee Network (SRI-Net) with a two-stage cross cross- attention structure to effectively capture the correlations among these attributes. The network subsequently integrates specular reflection related features with image features for final real/fake decision.
Our contributions can be summarized as follows:
1) We identify specular reflection as a robust forgery indicator due to its complex multi malti-parameter formulation, stronger nonlinearity and inherent difficulty replicate accurately.
2) We introduce a Retinex-based face texture estimation method that enables faster and more precise specular reflection extraction.
3) We design SRI-Net with two-stage cross-attention to exploit the relationships among specular reflection, face texture, and direct light.
4) Extensive experiments demonstrate superior performance on both traditional and generative deep- fake datasets.
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2
RELATED WORK
2.1
GENERAL FACE FORGERY DETECTION
Many previous studies have focused on general face forgery detection. From the perspective of feature extraction, these approaches can be broadly categorized into spatial-domain and frequency- domain approaches. Spatial-domani methods primarily analyze pixel-level forgery evidence and inconsistencies in forgery images. MesoNet (Afchar al. 2018) and Face X-ray (Li et al. 2020a) focused on mesoscopic properties or boundary discrepancies distinguish manipulated faces. Some 2022) employed techniques such adver- sarial training and reconstruction for enriching training data. Frequency-dome domann approaches (Masi (024) decomposed images into frequency components using Fourier or wavelet transforms to detect forgery traces that are less perceptible in the spatial domain. From the perspective of learning generalized representations, SBI (Shiohara & Yamasaki (022) and SLADD (Chen et al.) 2022b) explicitly minimized domain gaps to improve cross-dataset detection. Some works (Sun al.) 2020; Yan et al. 2024a) employed data augmen- tation and synthesis in latent space to boost model robustness. Some methods (Yan et al. 2023
works (Wang et al. 2020; Wang & Deng 2021 Cao et al.
al. 2020 Qian et al. 2020 Jeong et al. [022 Tan et al.
Dong et al. 2023) incorporated generalized forgery cues through specialized learning frameworks for more discriminative detection. Some methods (Cui et al. [205 Sun et al.) 2025 leverage pre- trained features from models such as CL.P. However, as forgery faces become increasingly realistic and forgery evidence is distributed unevenly across frequency bands, and with new forgery tech- niques continuously emerging, the effectiveness of existing detection methods declines.
2.2 ILLUMINATION BASED DETECTION
Several existing methods (Peng al. 2016 hu al. 2021 detected forgery evidence by extracting illumination cues from 3D disentangled faces under the Lambertian assumption, demonstrating no- table performance. Some works (Hu et al. 2021 Ebihara et al. 2020 (Siibold al. 2018 captured the local specular reflection the cornea nose for face forgery detection and face anti- mntii-spoffig. However, recent studies (Du al.] [023 Zhan al. 2024) revealed that diffusion models can effec- tively interpret geometric patterns and generate realistic shading estimations, thereby diminishing reliability of such illumination-bart detection approaches.
Nevertheless, the fundamental principle that attributes requiring more estimated parameters remain inherently more challenging to replicate remains unchanged. Given that illumination constitutes complex physical process, it warrants further exploration. Therefore, propose extending the Lambertian assumption to the more physically realistic Phong illumination model and precisely extract the complete and precise specular reflection component under 3D physical constraints, cap- turing the potential forgery evidence within it.
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Figure 2: The framework of proposed face forgery detection method. First, we 3DDFA to extract 3D shape and propose fast and accurate Retinex- method for texture extraction. Next, we employ spherical harmonic model to fit ambient and direct light, extracting specular reflection through residual based approach under Retinex-based texture constraints. We then propose the Epeeular-Reflectionn Reffection--nconnisteneey anconsistency-Netwee Network (SRI-Net) with a two-stage cross-attention structure to capture correlations among specular reflection, texture, and direct light. Finally, SRI-Net combines these specular reflection related features with image features for final real/fake decision.
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3 METHOD
In this section, we first analyze face illumination under the Phong illumination model and demon- strate that specular reflection is more difficult to replicate and contains more generalizable forgery evidence. Subsequently, we propose a Retinex theory based face texture extraction method to achieve faster and more accurate specular reflection extraction. Finally, we present SRI-Net with two-stage cross-attention architecture to capture the correlations of specular reflection, texture, and direct light, enabling more comprehensive forgery evidence extraction. The framework of our proposed method is illustrated in Fig. 2
3.1 FACE ILLUMINATION ANALYSIS
From the perspective of face generation, a face image is composed of 3D structure and color infor- mation:
I syy R(S, C),
where R. is the renderer, S is the 3D shape, and C represents the color information that combines texture (i.e., skin albedo) and illumination effects for each vertex on the 3D shape. The 3D shape is the easiest to replicate, as there is almost no occurrence of incompatible facial topology in existing forgery faces. While the color information C integrates both texture and illumination.
We posit that although state-of-the- forgery techniques have achieved remarkable realism, they remain constrained by fundamental principle machine learning: highly complex and non-linear functions are inherently more difficult for models learn accurately from finite data (Bishop & Nasrabadi 2006). This is reflected in the well-known biri-variance tradc-off, wherein approximat- ing a complex target function demands substantial model capacity and data, often resulting in failure modes in the most challenging sub-components.
Based on this principle, the illumination component, a complex physical process, is simultane- ously constrained by 2D local texture statistics, 3D global environmental conditions, and illumi- nation model constraints. Therefore, further investigation warranted to extract potential forgery evidence from illumination-related aspects of color information.
The Phong illumination model a widely used local illumination model that approximates the inter- action of light with surfaces by combining ambient light, direct light and specular reflection compo- nents. Ambient light is uniform and illuminates all surfaces equally, which ensures that objects are visible even in angle to the surface normal. Specular reflection creates shiny highlights, dependent on the surface's reflection of the light. Under Phong assumption, the RGB value for each vertex C₁ S is computed as follows:
not directly illuminated. Direct light is scattered across surface based on its
C = Amb T, (n; 1) Dir T, + vr.. Dir T;,
where Amb is ambient light, Dir direct light, is the texture (i.e., skin albedo), (r,, v) n Dir is specular reflection, (.) denotes the inner product operation, and * denotes the element-wise multiplication. n, is the vertex normal of the 3D shape, I is the direct light direction, r, is the reflection direction which can be computed by n is the specular exponent of surface material. It can be observed that among the three types of illuminations. the mathematical formulation of specular reflection is more complex, involving six estimated parameters and a stronger nonlinear representation in exponential form. This makes inherently more difficult to replicate.
2(n;, I)n; - 1. v is the viewer direction, and
Due to the fact that the Phong illumination model is a general model with clear physical significance, this characteristic where specular reflection is more difficult to replicate can be applied across vari- ous forgery methods, including current generative forgery techniques. Therefore, we posit that the specular reflection contains more generalizable forgery evidence.
3.2 SPECULAR REFLECTION EXTRACTION
To extract the specular reflection component, main steps involved according to Equ. 1) illumination and texture separation, and 2) illumination components separation.
Illumination and texture separation
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Existing methods typically perform illumination and texture separation using analysis-br by- synthesis approach within the 3D Morphable Model (3DMM) (Blanz & Vetter 2003). The illu- mination and texture parameterized through the spherical harmonic model (Zhagg & Samaras 2006) and the PCA texture model of Basel Face Model (BFM) (Paysan et al., 2009), with iterative optimization of the coefficients obtain the illumination and texture components. In spherical har- monic model, face color under arbitrary illumination can be represented by the linear combination:
I(S) = (Hy) T,
(3)
where H = [hi, hg...., ..., hn] denotes a set of harmonic reflectance functions forming orthonormal bases model illuiiatioo-induuedd brightness variations, while 7 = [71, 72, .... Yn. represents the n-dimensional reflectance parameters requiring estimation. Notably, h, is isotropic and h1 71 represent ambient light, whereas the directional components [hg "/,,... hy 799 represent direct light. Basis with n>>9 can be used to fit illumination with more detailed illumination, but the computational cost also increases significantly.
In PCA texture model of BFM, the texture is parameterized in the following form:
T Tofm + B,3,
(4)
where Torm is the mean face texture of BFM, is the 199 dimensional principle axes, B is the corresponding texture parameter. Based on the above equations, the parameters can be optimized through iterative methods by:
Argmin [[ Lyy (S. B)1l-
(S)
where I is the original face image. The estimated Hy and Tofm + B3 can represent the separated illumination and texture.
However, the PCA bases B are obtained by cigendecomposition of the covariance matrix of 200 3D face scans BFM, capturing dominant shape variations but failing to reconstruct identity fine- grained texture details due to dimensionality reduction and linear modeling constraints. Therefore, real-world scenario, the face texture should be formulated as follows:
T Tofm B3 + Tea-
where Tea is the identity texture details of corresponding face image. This indicates that the result derived from Equ. 5 contains a residual fine-grained texture detail Tho that the linear PCA model of the BFM fails capture in comparison to the real texture T.
Furthermore, inaccurate estimation of the texture will further lead to inaccurate estimation of the illumination. Particularly, due to the more complex and fine- mme-grained nature of the specular reflection, it is more significantly affected when constrained by an inaccurate texture. Therefore. it is necessary to propose new texture estimation method with a smaller residual to the real texture.
To address this issue, we propose Retinex theory (Land & McCann 1971 based texture extraction method for faster and more accurate estimation of specular reflection. Retinex theory is grounded in the physical model of image formation, which assumes that an observed image / (Ix, y) is the product of illumination [xx, y) and albedo R(x, y), expressed as (xx y) L(x,y) v) R(x, y).
To separate these components, Retinex employs logarithmic transformation, converting the multi- plicative relationship into an additive one:
log I (xx y) log L(x, y) + log R(x, y),
where the illumination L(x, 8) is typically estimated using low-pass filtering (c.g.. Gaussian filter- ing) of the input image. This choice is theoretically justified by the assumption that illumination varies spatially smoothly, while albedo contains high-frequency details such as identity texture. By applying Gaussian filter G., the illumination is approximated as G_ (I( y)), and the albedo can be obtained by:
log R(2, v) log [(x v) log Go v))]
(8)
where logarithmic form log R(z, y) is preferred over linear form R(x,y) to represent the texture in Retinex theory. This is because it avoids nonlinear exponential transformations that could rein- troduce illumination artifacts cause numerical instability. The log-domain processing maintains
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illumination invariance while better preserving fine surface details, making it more suitable for com- putational analysis of intrinsic surface properties under varying illumination conditions. Thus, the texture Tre of face image obtained by Retinex can be represented by the following formula:
Tre = log R.
Furthermore, to simultancously address both global and local illumination variations, we employ Multi-Scale Retinex (MSR) to enhance robustness under complex illumination conditions. Specifi- cally, large-scale Gaussian kernels are utilized to capture broad illumination gradients, while small- scale kernels supplement local illumination details, thereby enabling stable texture extraction across diverse illumination scenarios. The formula is presented as follows:
T mer
(log log (G,,(())).
(10)
where Go, denotes Gaussian kernels with varying scales o, and N represents the number of scales. This multi-scale integration ensures comprehensive illumination normalization while preserving fine-grained texture information. Thus, the illumination and texture separation process can be trans- formed from Equ. into the following formula:
Argmin, |[ (S. 7, mls
(11)
Notably, substituting T = T B3 with T T not only makes the texture estimation more accurate but also eliminates the necessity to estimate the 3 parameter. This approach circumvents the additional computational burden associated with the iterative optimization of both 3 and y pa- rameters required for the simultaneous optimization of illumination and texture. As result, the processing time for lingle-image illumination and texture separation is reduced from 0.78s to 0.29s, significantly improving efficiency and enabling rapid large-scale data processing.
Illumination components separation
Based on Equ. II the spherical harmonic coefficients can be estimated. An intuitive approach is to utilize h -71 as the ambient light, (2, ..., hy -Y9) as direct light, and higher order basis along with their coefficients to represent the specular reflection. However, this approach faces similar issues to the PCA model of BFM, namely that the specular reflection fitted using spherical harmonic bases still exhibits residuals when compared to the real specular reflection. Additionally, the estimation of higher order spherical harmonic coefficients introduces significant computation cost.
To address this issue, we propose separating the specular reflection through residual based ap- proach. We utilize the first nine basis functions of the spherical harmonic model to capture coarse illumination variations, which effectively fit the ambient light and direct light while being insensitive to fine-graine mne-ggained specular reflection. Therefore, the separation of specular reflection can be achieved in the following form:
SPR = (HH))))(-)) Tmar)/ /
(12)
where SPR is specular reflection. (H7)(1-9) represents the first nine spherical harmonic bases along with the coefficients estimated according to Equ. 11
Fig. (a) presents visualization of using (1) the mean texture Tofm and (2) the fitted texture Tof ff + B3 from BFM and (3) the Retieex-based texture T Tinsr as texture T. along with their corresponding obtained specular reflection, where the 3D shape is obtained by 3DDFA (Guo et al. (020)) in real- time. The results show that our proposed Tmer achieves more accurate texture estimation compared Tofm and T f + B3, thereby enabling fine-grained removal of identity texture from specular reflection. Fig. 3 (b) presents a comparison of detailed differences in specular reflection. The red bounding boxes demonstrate that specular reflection with T Tesr as constraint achieves more accurate removal of identity texture (c.g., in ocular and labial regions), while the green bounding boxes confirm that the extracted specular reflection better aligns with physical realism.
is worth noting that while Retinex theory has been previously applied in face forensics tasks (Chen et al. 2019; 2022a), their objective was to leverage Retinex- based image enhancement to reveal sub- tle dooaain artifacts in forged regions, treating the Retinex output the network. In our proposed method, the novelty lies not in the use of Retinex-basee texture es- timation itself, but in its application precision tool for 3D illumination decomposition. This
new input modality for

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Figure 3: The visualization of (a) Specular reflection estimation process under different types of texture constraints and (b) Comparison of specular reflection detailed difference.
enables more accurate estimation of specular reflection, thereby capturing more robust and physi- cally grounded forgery evidence.
3.3 SRI-NET
(SRI-Net) with Xception (Chollet) 2017) We propose Specular- wewai--eeetet--nnooooeeee ppeeuuarrffiiicooooooossseeeeeee as backbone, with architecture illustrated in Fig. [ As shown in Equ. [ the specular reflection intensity is determined by the light direction, 3D shape vertex normal, viewer direction, specular exponent of surface material, direct light intensity, and face texture. Therefore, forgery evidence should manifest not only in the specular reflection itself but also in its correlations with the afore- mentioned attributes. Given that human faces are all made of skin, the relative material properties be approximated through face texture.
To capture these correlations, we first flatten the specular reflection, texture, and direct light com- ponents into UV space normalize their directions. Subsequently, employ two-stage attention mechanism with residual connections to extract their correlations. The first stage captures the relationship between texture and direct light. Let ftex and fa denote the flattened feature maps of texture and direct light after the entry flow. The aggregated feature is computed as:
Stean Vd
faa Softmax
) Sat + frea + fai,
(13)
where d is the feature dimension, and Sea encodes both features and their correlations. The second stage captures the correlations between specular reflection and the texture-direct light features. Let fla be the processed fia through the middle flow, and fapr be the specular reflection features after passing through both entry and middle flows:
ftas
fst Softmax
Sapr + Sxpry
(14)
Vd
where fata aggregates the specular reflection features along with the correlations among specular reflection, texture, and direct light.
Meanwhile, since the original image retains the initial forgery evidence, we introduce an image branch to extract subtle information that may be distorted during specular reflection extraction, serv- ing as complementary component. Finally, we fuse the specular reflection related features with the image features form the feature vector for final real/fake decision.

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4 EXPERIMENTS
In this section, we provide a comprchensive evaluation of SRI-Net, covering various aspects such as datasets, detection performance comparisons, and ablation studies to demonstrate the effectiveness of SRI-Net.
4.1 DATASETS
Traditional datasets. We employ the FaceForensics** (FF++) [Rosler al. 2019), CelebDF_ v1, CelebDF_v2 v2 eL al., 2020b), and Deepfake (DFD) (Google 2019) datasets for evalu- ation. To simulate real-world conditions, utilize the FF++ c23 (light compression) version the
training set in subsequent experiments.
Generative datasets. We employ Diffusion Facial Forgery (DiFF) (Cheng al. 2024a) and DF40 Yan et al. 2024b) datasets for evaluation. DiFF dataset contains over 500,000 images synthe- sized using various generation methods (e.g.. Stable Diffusion XL (SDXL), Low-Rank Adaptation (LoRA)) across four categories: Text-to-lmage (T2I), Image-to-Image (I2I), Face Swapping (FS) and Face Editing (FE). Additionally, DF40 dataset contains forged data generated within the FF++ and CelebDF domains to ensure method diversity while maintaining consistent data distribution.
Evaluation Metrics. We utilize the Frame-Level Area Under Curve (AUC) metric on generative deepfake datasets, and utilize both frame-level and video-level AUC on traditional deepfake datasets.
4.2 COMPARISON ON TRADITIONAL DATASETS
Table I: Frame-level AUC (%) performance comparison on traditional Datasets.
Traditional Datasets
Method
Venue
CDF-v1
CDF-v2
DFD
Avg
FWA (Li & Lyu 2018)
CVPRW'T 18
79.0
66.8
74.0
73.3
CapsuleNet (gguyen el al.
2019
ICASSP'19 19
79.1
74.7
68.4
74.1
CNN-Aug (Haliassos al.
2022
CVPR'20
74.2
70.3
64.6
69.7
Face X-ray Li et 2020al
CVPR'20
70.9
67.9
76.6
71.8
FFD (Dang
2020
CVPR'20
78.4
80.2
77.7
F3Ner Qlan et al
2020
ECCV'20
77.7
79.8
70.2
75.9
SPSL Liu ct al.
CVPR'20
81.5
76.5
81.2
79.7
SRM [Luo et
2021
CVPR'21
79.3
75.5
81.2
78.7
CORE (Ni al.
2022
CVPRW'22
78.0
74.3
80.2
77.5
RECCE (Cao
2022
CVPR'22
76.8
73.2
81.2
77.1
SBI (hao et al.
2022
CVPR'22
81.3
77.4
UCF Yan
2023
ICCV'23
77.9
75.3
80.7
78.0
ED [Ba
2024
AAAF'4
81.8
86.4
ProDet Cheng
2024b
N4PS'24
90.9
84,2
84.8
86.6
LSDA Yan et 2024a)
CYPR'24
86.7
83.0
88.0
85.9
Fada (Cui al 2025
CVPR'22 '25
83.7
FIA-USA (Ma 2025
ARXIV'25
90.1
86.7
82.1
86.3
SRI-Net (Ours)
91.3
87.5
89.3
89.4
Table 2: Video-level AUC(%) performance comparison on traditional Datasets.
Traditional Datasets
Method
CDF-v2
DFD
Xception
81.6
89.6
EffiNet-B4
80.8
86.2
RECCE
82.3
89.1
F3-Net
78.9
84.4
SBI
90.6
88.2
UCF
83.7
86.7
FIA-USA
94.1
SRI-Net (Ours)
95.5
93.1
Tab. D and Tab. 2 present the frame-level and video-level AUC comparisons on traditional datasets, where video-level scores computed by averaging the frame-level scores across all frames. SRI-
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Net achieves state-of- ahe performance under both evaluation protocols. Specifically, it attains the highest AUC scores across all three datasets at the frame level, demonstrating its effectiveness in capturing subtle forgery evidence through explicit modeling of correlations among specular reflec- tion, texture, and direct light. At the video level, SRI-Net maintains its superior performance despite aggregation without exploiting temporal dependencies.
relying solely frame-wise
Table 3: Frame-level AUC (%) performance comparison DF40 Dataset.
DF40 Dataset
Method
Venue
uniface
c4s
facedancer
fsgan
inswap
simswap
Avg
RECCE
CVPR'22 22
84.2
65.2
78.3
88.4
79.5
73.0
78.1
SBI
CVPR'22
64.4
69.0
44.7
87.9
63.3
56.8
64.4
CORE
CVPRW'22
81.7
63.4
71.7
91.1
79.4
69.3
76.1
IID
CVPR'23
79.5
71.0
79.0
86.4
74.4
64.0
75.7
UCF
ICCV'23
78.7
69.2
80.0
88.1
76.8
64.9
76.3
LSDA
CVPR 24
85.4
68.4
75.9
83.2
81.0
72.7
77.8
CDFA
ECCV'24
76.5
67.4
75.4
84.8
72.0
75.4
ProgressiveDer
NIPS'24
84.5
71.0
73.6
86.5
78.8
77.8
78.7
FIA-USA
ARXIV'25
91.8
87.5
83.0
86.3
87.4
91.0
87.8
SRI-Net (Ours)
92.0
89.4
95.3
94.2
91.1
83.3
90.9
4.3
COMPARISON ON GENERATIVE DATASETS
Tab. 3 and Tab. present feaee-level AUC comparisons on the DF40 and DiFF datasets. For the six representative faceswap subsets in DF40 dataset, SRI-Net achieves substantial improvement in erage AUC from 87.8% to 90.9%. Similarly, on DiFF dataset, our method consistently outperforms existing approaches across all four subsets.
Notably, while conventional methods of- ten experience performance degradation on generative datasets due to the reduced texture inconsistencies. SRI-Net maintains robust detection performance. This re- silience can be attributed the fact that specular reflection is inherently difficult replicate accurately, and modeling their correlations with multiple facial attributes based on well established physical princi- ples.
Table 4: Frame-level AUC (%) performance compari- son on DiFF Dataset.
DiFF Dataset
Method
121
FS
FE
Avg
Xception
62.4
56.8
86.0
58.6
66.0
EffiNet-B4
74.1
57.3
82.1
57.2
67.6
F3-Net
66.9
67.7
81.0
60.6
69.1
SBI
80.2
80.4
85.1
68.8
78.6
FIA-USA
86.1
85.0
89.4
72.7
83.3
SRI-Net (Ours)
88.7
85.5
92.2
80.8
86.8
4.4 ABLATION STUDIES
In this section, conduct ablation studies to analyze the SRI-Net, the experimental results are presented in Tab.
Effectiveness of each branch. Rows 1-5 in Tab. 5 evaluuate individual branch contributions. 'Img", 'SPR', and 'MSR' denote the use of the original image, specular reflection, and MSR-based texture the sole input XceptionNet. 'Img SPR' refers using both the original image and specular reflection as inputs, with their feature vectors concatenated as the output. Meanwhile, 'Img + SPR MSR/Dir' corresponds to the SRI-Net architecture. It can be observed that SPR alone achieves considerable AUC performance. This strongly indicates that the specular reflection can effectively capture intrinsic forgery traces from images, which are discriminative in nature and demonstrate strong generalization across datasets. The combination of Img and SPR branches achieves superior performance, as the original image preserves undistorted forgery evidence while the SPR branch isolates difficult to replicate specular reflection that contains generalizable forgery evidence. MSR alone yields suboptimal performance. However, a marked improvement is observed when MSR is utilized in conjunction with the SPR and Dir (compare rows (4) and (5)). This contrast substantiates that the primary role of the MSR texture in our framework not as a standalone feature, but as key element in extracting the physically meaningful specular reflection inconsistency.

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Table 5: Ablation study results AUC (%) of SRI-Net.
Datasets
Method Settings
Celeb v2
DF40
DIFF
(1) Img
72.1
74.2
66.0
(2) SPR
78.8
73.9
(3) MSR
68.5
71.0
60.8
(4) Img SPR
84.5
87.2
83.9
(5) Img SPR MSR/Dir
87.5
90.9
86.8
(6) T Tafm
83.3
85,4
79.9
(7) Tafm + B3
88.2
82.5
(8) T TTee
87.5
90.9
86.8
(9) w/o shape norm
82.1
83.7
81.0
(10) w/ shape
87.5
90.9
86.8
(11) Original Image
87.5
90.9
86.8
(12) Gaussian Blurring
86.6
88.7
84.2
(13) JPEG Compression
87.1
90.2
86.4
(14) Gaussian Noise
87.0
88.9
85.5
(15) Cross-Attentic
87.5
90.9
86.8
(16) SE-block
86.9
88.6
84.5
(17) Concatenated
85.6
88.1
83.7
Effectiveness of Retinex-based Texture Extraction. Rows 6-8 compare three texture extraction methods (Fig. 3 The Retinex-based Taar achieves optimal performance by capturing fine-grained facial textures, enabling more precise separation of illumination components.
Effectiveness of shape norm. Rows 8-9 demonstrate that shape normalization improves perfor- mance by standardizing illumination directions, surface normals, and viewing directions, thereby enhancing the correlation capture between specular reflection, texture, and direct light components under the Phong illumination model.
Robustness to common post-processing. Rows 11-14 evaluate the robustness of our proposed SRI-Net under three types of common post-processing on test images: Gaussian Blurring, JPEG Compression, and Gaussian Noise. The results show that SRI-Net has demonstrated good stability, maintaining high AUC performance. This stability stems from its physics-driven design: while such distortions often degrade high-frequency forgery evidence used by spatial methods, SRI-Net targets deeper physical inconsistencies in specular reflection, direct light and texture. These viola- tions of illumination physics remain detectable even after image-level corruptions, ensuring more resilient forgery detection.
The choice of attention mechanism. Rows 15-17 compare fusion strategies for capturing incon- sistencies among specular reflection, direct light, and texture. Cross-attention outperforms both simple feature concatenation and -henhhi-weighting methods like the SE--lock. We attribute this to its ability to dynamically compute weighted interactions across all spatial locations, which al- lows the model discover and emphasize subtle, non-local inconsistencies between modalities. This provides a more expressive way to model their complex relationships compared to static or channel-level fusion operations.
5 CONCLUSION
In this paper, build upon the principle that face attributes requiring more estimated parameters and more complex physical constraints are inherently harder to replicate accurately, and demon- strate that the specular reflection component in the Phong illumination model contains generalizable forgery evidence. To enable accurate illumination separation, we develop faster and more accurate Retinex-bace -based texture extraction method that facilitates precise specular reflection estimation. We then propose SRI-Net to capture the correlations between specular reflection, face texture, and di- rect light components, thereby enabling the extraction of more complete forgery evidence. Overall, propose novel approach for detecting forgery evidence under physically realistic constraints, demonstrating superior performance on both traditional deepfake datasets and more challenging generative deepfake datasets.
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A APPENDIX
A.1 THE USE OF LLM
We used Large Language Models (LLMs) to assist with writing polishing and writing refinement during the preparation of this manuscript. The LLMs were employed solely for improving gram- mar, sentence structure, and overall readability. All research ideas, data analysis, conclusions, and substantive content remain entirely the original work of us. We take full responsibility for the accu- racy and integrity of all content presented in this paper.
A.2 IMPLEMENT DETAILS
For training specifications, we employ Xception as the backbone, optimized with the Adam opti- mizer at an initial learning rate of le-3 for 80 epochs. During training the FF++ dataset, uniformly sample 32 frames per video sequence, and the input size is 256x256. Data augmentation strategies include HorizontalFlip, RandomCutout and AddGaussianNosse The multiscale Retinex- based texture extraction method utilizes a values of [15, 80, 120] for face texture extraction.
A.3 EXAMPLES OF SPECULAR REFLECTION EXTRACTION
In this section, we present additional samples of specular reflection extraction Fig. We utilize 3DDFA for 3D shape extraction and propose fast and accurate Retinex- method for tex- ture extraction. Subsequently, we employ spherical harmonic model fit ambient light and direct light, extracting specular reflection through a residual-based approach under Retinex-based texture constraints.
Real samples
Fake samples
Image
3D shope
Retinex-based Texture
Ambient light
Direct light
Specular reflection
Figure 4: The visualization of Specular Reflection Extraction. The face image can be decomposed into 3D shape, Retinex- texture, ambient light, direct light, and specular reflection under Phong illumination model constraints. The samples on the left are real samples, while the samples on the right are fake samples.
A.4 ROBUSTNESS ACROSS SKIN TONES
This section investigates the impact of different skin tones on the performance SRI-Net. The evaluation is conducted the AI-Face dataset (Lin et al. 2025), which provides demographic at- tribute labels. The experimental results, summarized in Tab. 6 are reported for the test set stratified into three subsets: Light. Medium, and Dark, representing varying levels of skin tone. The results demonstrate that our method achieves superior and consistent performance across all three skin tone groups. This provides empirical evidence of its robustness skin tones.
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The stability can be explained through the Phong illumination model. Variations in skin tone pri- marily manifest as differences in the ambient light component of the model (as shown in Fig. 4 as this component interacts directly and uniformly with the skin's 's diffuse albedo. A key strength of our pipeline is its explicit separation of illumination. The Retinex-based texture extraction and subsequent spherical harmonic modeling collaboratively work isolate and filter out ambient light component. By effectively removing the primary carrier of skin tone information from the analysis, our method minimizes its sensitivity to such variations. The subsequent specular reflection analy- sis is therefore conducted on more normalized representation, leading to consistent performance across demographics. This design inherently decouples the detection from skin tone. We have added this analysis and the experimental results to the revised manuscript.
Table 6: AUC(%) performance comparison on AI- dataset.
Skin Tone
Method
Light
Medium
Dark
Xception (Chollet 2017
97.69
98.44
98.88
EftiNet-B4 Tan & Le 2019
99.23
98.94
97.59
F3-Net QQian
2020)
98.51
98.68
98.79
CORE (Ni al 2022
97.80
98.47
98.79
DAG- DAG-FDD (Ju et al. 20244
97.56
98.79
98.73
SRI-Net (Ours)
99.12
99.01
99.18
A.5 LIMITATION ANALYSIS AND FUTURE WORK
Fig. presents cases of misclassification. It can be observed that these cases characterized by extreme facial poses or significant self-occlusion on the surface, resulting in inaccurate 3D shape fitting and consequently imprecise specular reflection extraction.
To address this issue, our planned future work will focus on two key technical directions to enhance the robustness of the 3D shape estimation:
The first approach is to leverage recent advances in highly robust 2D facial landmark detection that achieve pixel-level accuracy in unconstrained environments (e.g., Pixel-in-Pixel Net (Jin al.) 2021)) to correct inaccuracies in the initial 3D shape. The core idea is to compute the displace- ment vectors between the projected vertices of the estimated 3D shape and the corresponding pixel- accurate 2D landmarks. These vertex-wise offsets can then be interpolated and applied to refine the entire 3D shape, effectively pulling it into better alignment with the image evidence, even under challenging poses. Based on these offsets, we can correct deviations in the 3D shape caused by extreme facial poses or occlusions.
The second approach involves adopting more advanced 3D shape estimation frameworks, such as 3DDFA-V3 (Wang et al., 2024), which utilizes segmentation information for face reconstruction. This provides more robust optimization objective than mere photometric alignment, leading to more stable and accurate 3D shapes in the presence of occlusions and extreme viewing angles.
A.6 STATISTICAL VISUALIZATION ANALYSIS OF SPECULAR REFLECTION
In this section, we conduct both PCA visualization and score distribution analysis the features extracted when using specular reflection as the sole input to XceptionNet (Row 2 of Tab. 55. eval- uated on the DiFF dataset. The results are shown in Fig. 6 and Fig. 7 The PCA visualization, which projects the original 2048-dimensional feature vectors onto a 2DD space, does not clearly re- veal distinct distribution patterns between real and fake faces. This limited visual separability expected given the model's AUC of 73.9% and the substantial information loss inherent in reduc- ing high-dimensional features to just two dimensions. However, our score distribution histogram demonstrates statistically significant divergence between real and fake samples. The clear sep- aration in score distributions provides compelling evidence that the specular reflection component captures discriminative forgery traces capable of effectively distinguishing between real and fake faces.
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Image
Retinex-based
Texture
Direct light
Specular
reflection
Figure 5: The visualization of misclassified cases. These cases characterized by extreme facial poses or severe self-occlusion of the facial surface.
Branch PCA Viculizationn (AUC 22.46) DIff Dataset
Ind
Figure 6: SPR branch feature PCA visualization.
SPm Branch Score Bistribution Motagram (AUCw75 991 Bill Dataset
Figure 7: SPR branch score distribution histogram.
17


Reviews:
[{"review_id": "HboJxeP9av", "reviewer": "Reviewer_NiAP", "summary": "This paper presents a physics-based face forgery detection method that exploits the difficulty of reproducing specular reflection in generative models. The authors propose a Retinex-based texture estimation for clean illumination decomposition, a residual spherical-harmonics model for efficient specular separation, and an SRI-Net that learns physical correlations among reflection, light, and texture.", "questions": "Specular reflection depends on surface properties. How stable is this method on faces with different skin tones (e.g., using AI-Face [3])?\n\n\n[3] AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark. CVPR 2025.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- This manuscript is well-structured and easy to follow.\n- This work provides a clear physical motivation by identifying specular reflection as the most complex component in generated faces, which is a reasonable choice grounded in physical principles.", "weaknesses": "- Retinex-based reflectance estimation has already been used in face forensics and related vision tasks[1,2] for exposing subtle forgery cues, so the usage of Multi-Scale Retinex is not entirely novel.\n- This work does not sufficiently validate robustness to common post-processing, such as Gaussian Blur and JPEG compression.\n\n[1] Attention-based Two-stream Convolutional Networks for Face Spoofing Detection. IEEE TIFS 2020.\n\n[2] Exposing Face Forgery Clues via Retinex-based Image Enhancement. ACCV 2022.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lHbcrRAA4m", "reviewer": "Reviewer_yPYK", "summary": "This manuscript presents a novel methodology for generalizable face forgery detection, underpinned by the observation that facial attributes governed by complex physical laws, particularly the specular reflection component, are inherently challenging for contemporary synthesis models to accurately replicate. The proposed approach systematically decomposes the facial image into fundamental physical components, including 3D shape, texture, and illumination, with the primary objective of isolating the specular reflection component for subsequent inconsistency analysis and detection. The core decomposition strategy employed is based on Retinex theory.", "questions": "1.Could you elaborate on what is meant by '3D global environmental conditions' (line 176, Section 3.1). It is vague.\n\n2.In Section 3.1 (line 185), there is a potential source of confusion regarding the notation in Formula (1) and Formula (2). Specifically, Formula (1) treats $C$ and $S$ as distinct variables, yet in Formula (2), $S$ is defined as being composed of elements $C_i$. The variable $C_i$ could easily be misinterpreted as a component of $C$, leading to ambiguity and obscuring the intended relationship between $C$, $S$, and $C_i$.\n\n3.For the title 'Illumination and texture separation' in Section 3.2 (line 205), is the intention to decouple the texture and illumination components that were implicitly combined within the variable $C$ as introduced in Formula(1)?\n\n4.The text spanning lines 236 to 247 in Section 3.2 should be broken down into smaller, logical paragraphs. The current dense formatting is difficult to read.\nRegarding Formula (6), is there a specific model or methodology suggested for obtaining the identity transformation matrix $T_{id}$? For example, could a pre-trained model such as ArcFace be employed for this purpose, or is an alternative approach required?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The manuscript is clearly written and easy to follow, with a concise method formulation and a framework that is simple yet demonstrably effective. The method achieves consistently strong results across a broad spectrum of face-swapping techniques, including both diffusion-based and GAN-based pipelines, suggesting good generalization beyond a single generator family. The ablation studies provide compelling evidence that each branch, as well as the method’s design/strategy choices, contributes positively to the overall performance.", "weaknesses": "1.In Section 3.2, the choice to employ Retinex theory and Multi-Scale Retinex (MSR) to achieve a 'smaller to the real texture' and enhance robustness is a major component of the methodology. However, these Retinex-based enhancement techniques are well-established, and their application alone does not appear to be a novel contribution. More critically, previous work [1], has utilized similar Retinex-based methods for image enhancement, yet this paper is not cited.\n\n2.The work is motivated by the claim that the decomposed space is more complex and thereby facilitates distinguishing real from fake; however, no statistical or theoretical evidence is provided, and the visual comparisons in Figs. 3 and 4 fail to substantiate this.\n\n[1] Chen, Han, Yuzhen Lin, and Bin Li. \"Exposing face forgery clues via retinex-based image enhancement.\" Proceedings of the Asian Conference on Computer Vision. 2022.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "XFq288Ruk7", "reviewer": "Reviewer_KKiD", "summary": "The paper proposes a novel approach for face forgery detection by leveraging the inherent difficulty of replicating specular reflection under the Phong illumination model. The authors introduce a faster and more accurate Retinex-based texture extraction method to enhance the separation of illumination components, particularly specular reflection. They design the Specular-Reflection-Inconsistency-Network (SRI-Net), which uses a two-stage cross-attention mechanism to capture correlations between specular reflection, face texture, and direct light. Experimental results demonstrate that the method achieves state-of-the-art performance on both traditional deepfake datasets and generative datasets, showcasing its robustness and generalizability.", "questions": "see above", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Novelty\n\nThe paper introduces a unique perspective by focusing on specular reflection as a forgery indicator, which is grounded in well-established physical principles. This approach provides a fresh direction in deepfake detection research.\nThe use of the Phong illumination model and Retinex theory for texture extraction is innovative and enhances the accuracy of specular reflection separation.\n\n\n- Strong Experimental Results\n\nThe method achieves superior frame-level and video-level AUC scores across traditional and generative datasets, outperforming existing state-of-the-art methods.\nThe robustness of the approach on high-quality generative datasets highlights its generalizability and resilience against subtle forgery evidence.\n\n- Efficiency\n\nThe Retinex-based texture extraction method significantly reduces processing time compared to traditional iterative optimization methods, making it suitable for large-scale data processing.", "weaknesses": "- Limited Analysis of Failure Cases\n\nWhile the paper briefly discusses misclassification due to extreme facial poses or occlusion, it does not provide detailed insights into how these issues could be mitigated or addressed in future work.\n\n- Dependence on 3D Shape Fitting\n\nThe method relies heavily on accurate 3D shape fitting to extract specular reflection. In scenarios with severe occlusion or extreme poses, the performance may degrade significantly. The paper does not explore alternative solutions or enhancements for these situations.\n\n- Complexity of Implementation\n\nAlthough the method is efficient in terms of processing time, the overall pipeline, including Retinex-based texture extraction, spherical harmonic modeling, and SRI-Net architecture, might be challenging to implement and deploy in real-world applications.\n\n- Limited Real-World Validation\n\nThe datasets used for evaluation, while diverse, may not fully represent the complexities of real-world deepfake scenarios. Additional experiments on real-world datasets or adversarially generated deepfakes could strengthen the paper's claims.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "u6gSEIagGn", "reviewer": "Reviewer_TtYa", "summary": "In this paper, the authors propose the hypothesis that specular reflection, a phenomenon governed by complex physical laws, is difficult for AI models to simulate, and therefore can serve as a key cue for generalizable deepfake detection. Specifically, they first employ Retinex for fast and accurate extraction of facial textures, then achieve improved specular reflection separation under the Phong illumination model. Building on these steps, they introduce the Specular-Reflection-Inconsistency Network (SRI-Net), which leverages a two-stage cross-attention mechanism to integrate relationships among specular reflection, facial texture, and direct lighting. Extensive experiments on both traditional and newly released generative deepfake datasets demonstrate that the proposed method outperforms existing approaches.", "questions": "Please see weaknesses.\n\nOverall, the reviewer thinks that this paper presents an interesting and promising perspective on leveraging physical properties of specular reflection for deepfake detection. However, the work would benefit from more comprehensive theoretical analysis to substantiate its central hypothesis and further strengthen the proposed framework.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper focuses on the physical properties of light–surface interaction, the proposed approach demonstrates improved robustness and universality.\n\n2. The authors replace the traditional BFM PCA-based texture estimation with multi-scale texture extraction based on Retinex theory, and design SRI-Net to effectively model the relationships among direct illumination, texture, and specular reflection.\n\n3. The method is thoroughly evaluated on multiple classic and state-of-the-art datasets, achieving significant performance gains on both frame-level and video-level metrics.", "weaknesses": "1. The central claim, i.e., “Specular reflection is hard to replicate”, is intuitive but not particularly novel. The paper does not provide a new theoretical explanation or quantitative physical analysis to substantiate why specular reflection is inherently more discriminative than other illumination features.\n\n2. Although the authors aim to capture the inconsistencies among specular reflection, texture, and direct illumination, they do not provide any mathematical formulation or quantitative metric defining how such inconsistencies are measured.\n\n3. Although the authors claim that specular reflection is difficult to counterfeit, their method depends on 3D shape estimation (via 3DDFA) and Retinex-based texture extraction, both of which may fail under unconstrained facial poses, occlusions, or complex lighting conditions. This limitation is only briefly mentioned in the appendix and lacks in-depth discussion or empirical validation under “in-the-wild” scenarios.\n\n4. The rationale behind the two-stage structure of SRI-Net and the choice of attention mechanism as the optimal modeling strategy is insufficiently explained and would benefit from clearer justification.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)