diff --git a/tools/mo/openvino/tools/mo/ops/group_norm.py b/tools/mo/openvino/tools/mo/ops/group_norm.py
index fa5bb290aaa..24395d5f3d3 100644
--- a/tools/mo/openvino/tools/mo/ops/group_norm.py
+++ b/tools/mo/openvino/tools/mo/ops/group_norm.py
@@ -18,3 +18,25 @@ class GroupNorm(Op):
             'in_ports_count': 3,
             'out_ports_count': 1,
         }, attrs)
+
+
+class GroupNormalization(Op):
+    op = 'GroupNormalization'
+    enabled = True
+
+    def __init__(self, graph: Graph, attrs: dict):
+        super().__init__(graph, {
+            'op': self.op,
+            'type': self.op,
+            'infer': copy_shape_infer,
+            'version': 'opset12',
+
+            'num_groups': None,
+            'epsilon': None,
+
+            'in_ports_count': 3,
+            'out_ports_count': 1,
+        }, attrs)
+
+    def backend_attrs(self):
+        return ['num_groups', 'epsilon']
diff --git a/tools/mo/openvino/tools/mo/ops/scatter.py b/tools/mo/openvino/tools/mo/ops/scatter.py
index c53693d0741..e761d9a35e1 100644
--- a/tools/mo/openvino/tools/mo/ops/scatter.py
+++ b/tools/mo/openvino/tools/mo/ops/scatter.py
@@ -27,6 +27,9 @@ class Scatter(Op):
             'infer': self.infer,
             'reverse_infer': lambda node: reverse_bypass_infer(node, in_ports=[0]),
 
+            'reduction': None,
+            'use_init_val': None,
+
             'in_ports_count': 4,
             'out_ports_count': 1,
         }
@@ -85,6 +88,13 @@ class ScatterElementsUpdate(Scatter):
     op = op_type = 'ScatterElementsUpdate'
     version = 'opset3'
 
+    def backend_attrs(self):
+        version = self.get_opset()
+        if version == 'opset12':
+            return ['reduction', 'use_init_val']
+        else:
+            return []
+
     @staticmethod
     def infer(node: Node):
         Scatter.infer(node)
@@ -111,7 +121,9 @@ class ScatterElementsUpdate(Scatter):
             ''.format(node_name, indices_shape, updates_shape)
 
         axis = node.in_port(3).data.get_value()
-        if input_value is not None and indices_value is not None and updates_value is not None and axis is not None:
+        opset = node.soft_get('version', 'default')
+        is_opset12_reduction = opset == 'opset12' and (node.soft_get('reduction') != 'none' or not node.soft_get('use_init_val'))
+        if input_value is not None and indices_value is not None and updates_value is not None and axis is not None and not is_opset12_reduction:
             assert axis.size == 1, "The node {} has axis input value size equal to {} but it should be exactly 1.".format(
                 node_name, axis.size)
             axis = axis.item()
diff --git a/tools/mo/unit_tests/mo/utils/ir_reader/ops_test.py b/tools/mo/unit_tests/mo/utils/ir_reader/ops_test.py
index aa1f690f4a0..2df88dab8bd 100644
--- a/tools/mo/unit_tests/mo/utils/ir_reader/ops_test.py
+++ b/tools/mo/unit_tests/mo/utils/ir_reader/ops_test.py
@@ -6,6 +6,7 @@ import tempfile
 import numpy as np
 from pathlib import Path
 
+import openvino.runtime.opset12 as opset12
 import openvino.runtime.opset11 as opset11
 import openvino.runtime.opset10 as opset10
 from openvino.runtime import Model, serialize, Core, PartialShape, Dimension
@@ -207,3 +208,40 @@ class TestOps(unittest.TestCase):
         graph = TestOps.check_graph_can_save(model, 'scatter_dynamic_model')
         scatter_update_node = graph.get_op_nodes(op="ScatterUpdate")[0]
         self.assertListEqual(scatter_update_node.out_port(0).data.get_value().tolist(), [0, None])
+
+    def test_pad_12(self):
+        data_parameter = opset12.parameter([6, 12, 10, 24], name="Data", dtype=np.float32)
+        pad = opset12.pad(data_parameter, np.int64([0, 0, -1, -2]), np.int64([0, 0, -3, -4]), "constant")
+        model = Model(pad, [data_parameter])
+        graph = TestOps.check_graph_can_save(model, 'pad_model')
+        pad_node = graph.get_op_nodes(op="Pad")[0]
+        self.assertEqual(pad_node["version"], "opset12")
+        self.assertListEqual(pad_node.in_port(1).data.get_value().tolist(), [0, 0, -1, -2])
+        self.assertListEqual(pad_node.in_port(2).data.get_value().tolist(), [0, 0, -3, -4])
+        self.assertListEqual(pad_node.out_port(0).data.get_shape().tolist(), [6, 12, 6, 18])
+
+    def test_scatter_elements_update_12(self):
+        data_parameter = opset12.parameter([10], name="Data", dtype=np.float32)
+        scatter = opset12.scatter_elements_update(data_parameter, np.int32([5, 0, 7, 5]), np.float32([5., 6., 1.5, -5.]), np.int32(0), "sum", False)
+        model = Model(scatter, [data_parameter])
+        graph = TestOps.check_graph_can_save(model, 'scatter_model')
+        scatter_node = graph.get_op_nodes(op="ScatterElementsUpdate")[0]
+        self.assertListEqual(scatter_node.out_port(0).data.get_shape().tolist(), [10])
+        self.assertEqual(scatter_node["version"], "opset12")
+        self.assertEqual(scatter_node['reduction'], 'sum')
+        self.assertFalse(scatter_node['use_init_val'])
+
+    def test_group_norm_12(self):
+        data_parameter = opset12.parameter([1, 3, 3, 3], name="Data", dtype=np.float32)
+        scale = np.array((1, 1, 1), dtype=np.float32)
+        bias = np.array((1, 1, 1), dtype=np.float32)
+        num_groups = 1
+        epsilon = 1e-6
+        node = opset12.group_normalization(data_parameter, scale, bias, num_groups, epsilon)
+        model = Model(node, [data_parameter])
+        graph = TestOps.check_graph_can_save(model, 'group_norm_model')
+        gn_node = graph.get_op_nodes(op="GroupNormalization")[0]
+        self.assertListEqual(gn_node.out_port(0).data.get_shape().tolist(), [1, 3, 3, 3])
+        self.assertEqual(gn_node["version"], "opset12")
+        self.assertEqual(gn_node['num_groups'], 1)
+        self.assertEqual(gn_node['epsilon'], 1e-06)